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Record W4391874370 · doi:10.34119/bjhrv7n1-393

Incidência de acidentes ofídicos no estado do Pará

2024· article· pt· W4391874370 on OpenAlexaff
Arthur Henrique Lima Bastos, Giovanna Niek Streithorst, Gabriella dos Santos Freitas, Anabella Peralta da Rocha, Nicolle Barros Wan-Meyl

Bibliographic record

VenueBrazilian Journal of Health Review · 2024
Typearticle
Languagept
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsCascades (Canada)
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

No estado do Pará devido há especificidades geográficas há uma notória incidência de acidentes ofídicos. Os acidentes ofídicos são desencadeados por animais peçonhentos os quais produzem a peçonha em um grupo de células ou órgão secretor. Esses animais possuem uma ferramenta para injetar a peçonha no seu alvo. Diante dos números elevados e crescentes de casos além da gravidade desses acidentes evidencia-se uma especial preocupação com o estado do Pará. Tal acidente pode ocorrer devido a atividades diárias, não observância de normas de segurança, descarte inadequado do lixo, sazonalidade. Além disso, a subnotificação dos casos de acidente também se torna um grande problema no combate através de saúde pública adequada.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.091
GPT teacher head0.503
Teacher spread0.413 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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