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Record W4391227509 · doi:10.1186/s40779-024-00509-8

Nickel’s carcinogenicity: the need of more studies to progress

2024· letter· en· W4391227509 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

VenueMilitary Medical Research · 2024
Typeletter
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsChildren's Hospital of Eastern OntarioStollery Children's HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCarcinogenNickelMetallurgyBiochemistry

Abstract

fetched live from OpenAlex

On March 8-15, 2022, a board of international scientists assembled in Lyon to evaluate the carcinogenicity of cobalt metal, cobalt(II) salts, antimony trioxide, and weapons-grade tungsten alloy harboring nickel and cobalt [1].The 131st International Agency for Research on Cancer (IARC) Monograph is the result of a 6-9month work of perusing the literature, slide evaluation, data interpretation, and interim meetings.The assessment of cobalt, antimony, and nickel-containing alloys will have tremendous consequences for the industry, health, and defense departments [1].Armor-penetrating projectiles utilize tungsten alloys of weapons-grade quality, consisting of 91-93% tungsten, 2-4% cobalt, and 3-5% nickel.Inhalation of hazardous substances can occur because of occupational exposure in weapons production.Both military personnel and civilians may encounter nickel-containing metal aerosols that are generated during the firing or impact of weapons.Long-term exposure to residual embedded fragments from munitions can pose significant hazards.The available exposure data were limited, nevertheless, the

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.082
GPT teacher head0.410
Teacher spread0.328 · 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