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Record W7099938622

Review CMAJ

2014· article· en· W7099938622 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCause of deathPoison controlCommon cause and special causeMotor vehicle crashInjury prevention
DOInot available

Abstract

fetched live from OpenAlex

Trauma caused by motor vehicle colli-sions is a common cause of death,accounting for more than 3000 deaths each day worldwide (about 7 of which occur in Canada).1 For each person who dies, an addi-tional 80 people sustain injuries and 300 suffer financial losses.2 Trauma from motor vehicle collisions places a substantial load on emer-gency services and collectively costs Canada more than $100 million per day in total societal expenditures.3 About 50 % of related deaths occur within minutes of impact, reinforcing the need for effective prevention strategies.4 Motor vehicle collisions are projected to be the fifth leading cause of death overall by the year 2020 and are currently the most common cause of death in Canada from birth to middle age.5,6

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2710.124

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 head0.013
GPT teacher head0.279
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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