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Record W3084505844

Degradation of Fingernail Composition from Exposure to Industrial Chemicals

2020· article· en· W3084505844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBleachSodium hydroxideChemistryChemical compositionEnvironmental chemistryAcetoneSodium hypochloriteCleaning agentPulp and paper industryToxicologyOrganic chemistryBiology
DOInot available

Abstract

fetched live from OpenAlex

The application of fingernails as biomarkers have increased within forensic science as a better tissue sample to analyze when it comes to chemical exposure and biological substances being accumulated within fingernails. Due to their structure and properties, they have the ability to retain a discrete record of detailed information on drug use, pathology, diet and location history as well as exposure to explosives residues, occupational chemicals or other pollutants. This research observed how certain industrial chemicals affect the composition of fingernails when exposed to them for a certain prolonged period of time Hydrochloric acid was the most destructive chemical used, degrading fingernail samples within a week. Sodium hydroxide was the second most destructive chemical, where samples after week 1 became degraded. Sulfuric acid was the third most destructive chemical, degrading samples after week 3. Paint and cyanoacrylate did not degrade samples but concealed all morphological features. Acetone and bleach had an insignificant effect in degradation.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.336
Teacher spread0.237 · 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

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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