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Record W3017975870 · doi:10.5114/amsik.2019.94230

Not all the people of the Department of Forensic Medicine in Lublin (1945–1949)

2019· article· en· W3017975870 on OpenAlexaboutno aff
Wojciech Chagowski

Bibliographic record

VenueArchives of Forensic Medicine and Criminology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolish Historical and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsForensic scienceHistoryArchaeology

Abstract

fetched live from OpenAlex

ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Chagowski W. Not all the people of the Department of Forensic Medicine in Lublin (1945–1949). Archiwum Medycyny Sądowej i Kryminologii/Archives of Forensic Medicine and Criminology. 2019:137-144. doi:10.5114/amsik.2019.94230. APA Chagowski, W. (2019). Not all the people of the Department of Forensic Medicine in Lublin (1945–1949). Archiwum Medycyny Sądowej i Kryminologii/Archives of Forensic Medicine and Criminology, 137-144. https://doi.org/10.5114/amsik.2019.94230 Chicago Chagowski, Wojciech. 2019. "Not all the people of the Department of Forensic Medicine in Lublin (1945–1949)". Archiwum Medycyny Sądowej i Kryminologii/Archives of Forensic Medicine and Criminology: 137-144. doi:10.5114/amsik.2019.94230. Harvard Chagowski, W. (2019). Not all the people of the Department of Forensic Medicine in Lublin (1945–1949). Archiwum Medycyny Sądowej i Kryminologii/Archives of Forensic Medicine and Criminology, pp.137-144. https://doi.org/10.5114/amsik.2019.94230 MLA Chagowski, Wojciech. "Not all the people of the Department of Forensic Medicine in Lublin (1945–1949)." Archiwum Medycyny Sądowej i Kryminologii/Archives of Forensic Medicine and Criminology, 2019, pp. 137-144. doi:10.5114/amsik.2019.94230. Vancouver Chagowski W. Not all the people of the Department of Forensic Medicine in Lublin (1945–1949). Archiwum Medycyny Sądowej i Kryminologii/Archives of Forensic Medicine and Criminology. 2019:137-144. doi:10.5114/amsik.2019.94230.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.305
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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