Indeks tjelesne mase arheoloških populacija dobar je indikator nutritivnog opterećenja organizma te može uputiti na kvalitetu života i zdravlje pojedine populacije i služiti kao usporedba među ...populacijama. U radu su analizirani koštani ostatci s arheoloških nalazišta s područja istočne obale Jadrana datirani u razdoblja od antike do novog vijeka. Iako je riječ o relativno malom uzorku, rezultati istraživanja pokazali su smanjenje kvalitete života, odnosno tjelesne mase u muškaraca, i to u razdobljima razvijenog i kasnog srednjeg vijeka, a što je u skladu s prethodnim istraživanjima hrvatske populacije. Žene su tijekom svih razdoblja imale sličnu, konstantnu tjelesnu masu, što se osim raspodjelom rada i dostupnosti hrane može objasniti i hormonskim
utjecajima te drukčijim metabolizmom masti. Indeks tjelesne mase pokazao se kao dobar indikator za dopunu spoznaja o kvaliteti života i zdravlju arheoloških populacija.
To investigate the usefulness of humerus measurement for sex determination in a sample of medieval skeletons from the Eastern Adriatic Coast. Additional aim was to compare the results with ...contemporary female population.
Five humerus measurements (maximum length, epicondylar width, maximum vertical diameter of the head, maximum and minimum diameter of the humerus at midshaft) for 80 male and 35 female medieval and 19 female contemporary humeri were recorded. Only sufficiently preserved skeletons and those with no obvious pathological or traumatic changes that could affect the measurements were included. For ten samples, analysis of DNA was performed in order to determine sex using amelogenin.
The initial comparison of men and women indicated significant differences in all five measures (P<0.001). Discriminant function for sex determination indicated that as much as 85% of cases could be properly categorized, with better results in men (86%) than women (80%). Furthermore, the comparison of the medieval and contemporary women did not show significant difference in any of the measured features. Sex results obtained by anthropological and DNA analysis matched in all 10 cases.
The results indicate that humerus measurement in Croatian medieval population may be sufficient to determine the sex of the skeleton. Furthermore, it seems that secular changes have not substantially affected contemporary population, suggesting that the results of this study are transferable to contemporary population as well.
Sažetak
Procjena spola jedan je od prvih koraka u forenzičkoj identifkaciji ljudskih koštanih ostataka te
je za hrvatsku populaciju potrebno razviti pouzdane metode za procjenu spola, uzevši u obzir, ...u
prethodnim radovima dokazanu, populacijsku specifčnost. Osim u manjem broju istraživanja,
do sada nije istražen spolni dimorfzam za hrvatsku populaciju (osim za bedrene i goljenične
kosti, prsnu kost te orbite). Stoga je cilj ovoga istraživanja bio analizirati spolni dimorfzam
mjere nagiba čeone kosti na suvremenoj hrvatskoj populaciji s pomoću snimaka kompjutorizirane tomografje (MSCT). Odabrana je mjera nagiba čeone kosti jer je navedeni nagib važan i
u morfološkoj procjeni spola, no do sada se nije kvantifcirao. Ukupno je u istraživanje uključeno
180 MSCT snimaka, odnosno 180 lubanja (90 muškaraca i 90 žena) poznatoga spola i dobi.
Izmjeren je frontalni (glabelarni) kut – onaj koji zatvaraju pravac koji prolazi glabelom, a paralelan je s frankfurtskom horizontalom, i tangenta na obris čeone kosti. Testiran je spolni dimorfzam, pri čemu je razina statističke značajnosti postavljena na P < 0,05. Za dobivanje modela
za klasifkaciju spola rabljena je diskriminantna funkcijska analiza. Mjera nagiba čeone kosti
pokazala je statistički značajan spolni dimorfzam (P < 0,001). Dobivena je točnost procjene
spola za muškarce i žene te za ukupni uzorak od 91,3%. To je istraživanje pokazalo da je spolni
dimorfzam nagiba čeone kosti u suvremenoj hrvatskoj populaciji iznimno izražen te da se može
rabiti u forenzičkim slučajevima. Istraživanje je također skrenulo pozornost na važnost uporabe
medicinskih snimaka u forenzici te na uključivanje nestandardnih mjera u izradu standarda za
procjenu spola.
procjena spola, moderni uzorak, forenzika, MSCT, nagib čeone kosti
Abstract
ChatGPT-3.5, an AI language model capable of text generation, translation, summarization, and question-answering, has recently been released for public use. Studies have shown it can ...generate abstracts, research papers, and dissertations, and create quality essays on different topics. This led to ethical issues in using ChatGPT in academic writing, AI authorship, and evaluating students’ essays. However, it is still unknown how ChatGPT performs in students’ environments as a writing assistant tool and if it enhances students’ essay-writing performance. In the present study, we examined students’ essay-writing performances with or without ChatGPT as an essay-writing assistance tool. The average essay grade was C for both control (traditional essay-writing,
n
= 9) and experimental (ChatGPT-assisted essay-writing,
n
= 9) groups. None of the predictors affected essay scores: group, writing duration, study module, and GPA. The text unauthenticity was slightly higher in the experimental group, but the similarity among essays was generally low in the overall sample. In the experimental group, the AI classifier recognized more potential AI-generated texts. Our results demonstrate that the ChatGPT group did not perform better in either of the indicators; the students did not deliver higher quality content, did not write faster, nor had a higher degree of authentic text. We anticipate that these results can relieve some concerns about this tool’s usage in academic writing. ChatGPT-assisted writing could depend on the previous knowledge and skills of the user, which might, in certain instances, lead to confusion in inexperienced users and result in poorer essay writing performance.
To examine seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in industry workers population sample.
From 23 to April 28, 2020, we conducted serological testing ...for antibodies (Immunoglobulin G (IgG) and Immunoglobulin M (IgM)) on 1494 factory employees living in the Split-Dalmatia and Šibenik-Knin County (Croatia).
We detected antibodies in 1.27% of participants (95% confidence interval CI 0.77-1.98%). In Split facility 13/1316 (0.99%, 95% CI 0.53-1.68%) of participants were tested positive, of which 13/1079 (1.20%, 95% CI 0.64-2.05%) of those living outside the facility and 0/237 (0%, 95% CI 0-1.26%) of those living inside the facility. In Knin facility, 6/178 (3.37%, 95% CI 1.25-7.19%) participants were tested positive for antibodies.
The study showed relatively small SARS-CoV-2 antibody seroprevalence in the DIV Group population sample.
The osteometric methods are the most reliable way to estimate the sex of skeletons when DNA analysis is not used. However, as osteometric studies usually ignore the overlap in male and female ...skeletal dimensions, they rarely achieve accuracy sufficient for forensic application. To resolve this issue, recent studies suggest sex estimation only when posterior probability (pp) is greater than 0.95, but that approach does not always provide sufficient accuracy and creates a large proportion of unsexed skeleton. Thus, our study aimed to explore whether it is possible to adjust pp on skeletal measurements with pronounced sexual dimorphism to meet 95% accuracy and to enable sex estimation on a reasonable proportion of individuals. From 207 skeletons, we included 65 postcranial measurements and selected 10% of variables with the highest sexual dimorphism. We computed univariate and bivariate discriminant functions using pp threshold of 0.5, 0.95, and the threshold required to achieve accuracy of ≥ 95%. Discriminant functions with pp=0.5 obtained accuracy of 85%–93%, while those with pp≥0.95 and adjusted posterior probabilities obtained 94%–99%. However, we showed that by selecting a particular threshold, sex could be estimated on a greater proportion of individuals than for pp≥0.95: 42%–86% vs. 24%–62% for univariate and 69%–95% vs. 49%–78% for bivariate functions. Therefore, when developing sex estimation models, we suggest not to use fixed pp level, but to adjust pp to achieve 95% accuracy and to minimize the percentage of unsexed skeletons.
•We developed a binary classification method for optimizing classification thresholds.•As example data, we used three male and female handprint measurements (n=160).•We tested classification ...performances of LDA by traditional and proposed approach.•Traditional approach provided accuracies 78.7–92.5 % and PPVs/NPVs 78.2–93 %.•The newly proposed approach provided accuracies ≥ 95 % and PPVs/NPVs ≥ 95 %.
Binary classification techniques are commonly used in forensic examination to test if a specimen belongs to a particular group and base the expert opinion on the questioned evidence. However, most of the currently used methods do not achieve sufficient accuracy due to the ignoring of the specimens classified in the overlapping area. To address the issue, we proposed a novel Adjusted binary classification (ABC) algorithm that automatically adjusts posterior probabilities to reach classification accuracy and positive/negative predicted values (PPV, NPV) of 95 %.
In the presented example, we used three handprint measurements from 160 participants (80 males and 80 females) to develop models that would classify sex from their dimensions. The sample was split into the training/cross-validated (70 %) and testing sample (30 %). We developed four classification models using linear discriminant analysis (LDA) by employing traditional single cut-off values and ABC approach that for each group provides a specific posterior probability cut-off threshold.
In the cross-validated sample, the accuracy of traditional models was 78.7–92.5 %, while PPVs/NPVs ranged between 78.2 and 93 %. ABC models provided 95 % accuracy, PPV, and NPV, and could classify 35.5–88.1 % of specimens. In the testing sample, ABC models achieved accuracy of 97.3–100 %, PPV/NPV 95.4–100 %, and could be applied to 29.1–87.5 % of specimens.
The study demonstrated that the ABC approach could adjust classification models to reach predefined values of accuracy, PPV, and NPV. Therefore, it could be an efficient tool for conducting a binary classification in forensic settings and minimizing the possibilities of incorrect classifications.
The study aimed to test the applicability of the Probabilistic Sex Diagnosis (DSP) method in the bioarchaeological context by validation with known sex data obtained by aDNA analysis on the medieval ...samples from the Eastern Adriatic coast. We tested the method on 57 skeletons of known sex using 30 different combinations of measurements. The possibility of sex estimation ranged from 35.90 to 86.11% depending on the combinations used while sexing accuracy ranged from 92.86 to 100%. Females were classified correctly in all cases, and only one male was misclassified in all combinations that could be tested. Accuracy rates higher than 95% were obtained for every combination where the number of available measurements was larger than 15. Therefore, we encourage further validation of the method on different ancient populations and implementation of the method for creating reference sex data and development of metric and non-metric population-specific sex estimation standards.
•We tested DSP method by comparison to known sex data obtained by aDNA analysis.•Sample consisted of 57 skeletons from medieval Eastern Adriatic coast sites.•Depending on chosen measurements sex could be estimated in 35.90–86.11% cases.•Sexing accuracy ranged from 92.86 to 100%.•We suggest DSP method for creating reference data for sex in ancient populations.
The adjusted binary classification (ABC) approach was proposed to assure that the binary classification model reaches a particular accuracy level. The present study evaluated the ABC for osteometric ...sex classification using multiple machine learning (ML) techniques: linear discriminant analysis (LDA), boosted generalized linear model (GLMB), support vector machine (SVM), and logistic regression (LR). We used 13 femoral measurements of 300 individuals from a modern Turkish population sample and split data into two sets: training (n = 240) and testing (n = 60). Then, the five best-performing measurements were selected for training univariate models, while pools of these variables were used for the multivariable models. ML classifier type did not affect the performance of unadjusted models. The accuracy of univariate models was 82−87%, while that of multivariate models was 89−90%. After applying ABC to the crossvalidation set, the accuracy and the positive and negative predictive values for uni- and multivariate models were ≥95%. Sex could be estimated for 28−75% of individuals using univariate models but with an obvious sexing bias, likely caused by different degrees of sexual dimorphism and between-group overlap. However, using multivariate models, we minimized the bias and properly classified 81−87% of individuals. A similar performance was also noted in the testing sample (except for FEB), with accuracies of 96−100%, and a proportion of classified individuals between 30% and 82% in univariate models, and between 90% and 91% in multivariate models. When considering different training sample sizes, we demonstrated that LR was the most sensitive with limited sample sizes (n < 150), while GLMB was the most stable classifier.
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