The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Evaluation of glass samples for forensic purposes-an application of likelihood ratios and an information–theoretical approach
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There is a lack of a study on the impact of adverse conditions on calibration in facial image comparison.
“There is a lack of a similar study in case of facial image comparison.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [48]
Section 3.4.1 examines how mismatched glass databases affect calibration, so this paper is an example of the kind of study the citing sentence calls “similar.” It does not address facial image comparison or establish that such a study is lacking in that field. (LLM, conf=0.80)
Experimental — accuracy not yet measuredIntegration of advanced machine-learning algorithms for the compensation of adverse conditions into forensic evaluation helps.
“The analysis of [48] shows that integration of advanced machine-learning algorithms for the compensation of adverse conditions into forensic evaluation helps in this direction.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [48]
Adverse conditions greatly affect calibration performance.
“They find this impact greatly affects calibration performance.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [48]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Citations
Cites
The Comparison and Evaluation of Forecasters
DeGroot, M. H., & Fienberg, S. E. (1983) · The Statistician · doi:10.2307/2987588 · Cited by 604
Flagged first, then by citations
Research works printed but not linked: 15
- Zadora, 2003, SEM-EDX — a useful tool for forensic examinations, Material Chemistry and Physics, 81, 345, 10.1016/S0254-0584(03)00018-X · DOI
- Hicks, 2003, The classification and discrimination of glass fragments using non destructive energy dispersive X-ray fluorescenc, Forensic Science International, 137, 107, 10.1016/S0379-0738(03)00278-0 · DOI
- Trejos, 2005, Strategies for the analysis of glass fragments by LA-ICP-MS Part 1: micro-homogeneity study of glass and its application to the interpretation of forensic evidence, Talanta, 67, 388, 10.1016/j.talanta.2005.01.042 · DOI
- Aitken, 2006, Evaluation of trace evidence for three-level multivariate data with the use of graphical models, Computer Statistics and Data Analysis, 50, 2571, 10.1016/j.csda.2005.04.005 · DOI
- Aitken, 2007, A two-level model for evidence evaluation, Journal of Forensic Sciences, 52, 412, 10.1111/j.1556-4029.2006.00358.x · DOI
- Zadora, 2009, Classification of glass fragments based on elemental composition and refractive index, Journal of Forensic Sciences, 54, 49, 10.1111/j.1556-4029.2008.00905.x · DOI
- Zadora, 2010, Two-level model for evidence evaluation in the presence of zeros, Journal of Forensic Sciences, 55, 371, 10.1111/j.1556-4029.2009.01316.x · DOI
- Zadora, 2010, Evidential value of physicochemical data — comparison of methods of glass database creation, Journal of Chemometrics, 24
- Evett, 1998, Towards a uniform framework for reporting opinions in forensic science casework, Science and Justice, 38, 198, 10.1016/S1355-0306(98)72105-7 · DOI
- Aitken, 2004, Evaluation of trace evidence in the form of multivariate data, Applied Statistics, 53, 109
- Zieba-Palus, 2008, Differentiation and evaluation of evidence value of styrene acrylic urethane topcoat car paints analysed by pyrolysis-gas chromatography, Journal of Chromatography A, 1179, 47, 10.1016/j.chroma.2007.09.045 · DOI
- Evett, 1996, Statistical analysis of STR data, Vol. 6, 79
- Brümmer, 2006, Application independent evaluation of speaker detection, Computer Speech and Language, 20, 230, 10.1016/j.csl.2005.08.001 · DOI
- D. Ramos, Forensic evaluation of the evidence using automatic speaker recognition systems, Ph.D. thesis, Depto. de Ingenieria Informatica, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain, available at http://atvs.ii.uam.es (2007).
- Zadora, 2009, Likelihood ratio model for classification of forensic evidences, Analitica Chimica Acta, 64, 266, 10.1016/j.aca.2008.12.013 · DOI
Cited by
Sorted by citations
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Rodriguez, A. M., Geradts, Z., & Worring, M. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111239 · Cited by 19
Metadata
- Authors
- Zadora, G., & Ramos, D.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 102
- Issue
- 2
- Pages
- 63-83
- ISSN
- 0169-7439
- Cited by
- 48
- References
- 1
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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