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Record W2895205570 · doi:10.7589/2018-05-118

A DETERMINANTS OF HEALTH CONCEPTUAL MODEL FOR FISH AND WILDLIFE HEALTH

2018· article· en· W2895205570 on OpenAlexafffund
Julie Wittrock, Colleen Duncan, Craig Stephen

Bibliographic record

VenueJournal of Wildlife Diseases · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversity of Saskatchewan
FundersPublic Health Agency of CanadaCanadian Natural Resources Limited
KeywordsBiologyWildlifeFish <Actinopterygii>Conceptual modelFisheryEnvironmental resource managementEnvironmental planningEcologyGeographyComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

Our objectives were to establish if the determinant of health model used in the fields of human population and public health could be adapted to wildlife health; if it was applicable to more than one species; and if it reflected how fish and wildlife managers conceptualized health in practice. A conceptual model was developed using a scoping review on fish and wildlife health and resilience coupled with a participatory process with experts on barren ground caribou ( Rangifer tarandus groenlandicus) and sockeye salmon ( Oncorhynchus nerka) health. Both the literature and experts supported the concept of wildlife health as a cumulative effect involving multiple factors that extend beyond the disease and pathogen focus of many wildlife health studies and legislation. Six themes were associated with fish and wildlife health: 1) the biologic endowment of the individual and population; 2) the animal's social environment; 3) the quality and abundance of the animal's needs for daily living; 4) the abiotic environment in which the animal lives; 5) sources of direct mortality; and 6) changing human expectations. These themes were shared between salmon and caribou and conformed to expert perceptions of health. Determinants of health used in human public health are used for planning, development of policy, and guiding of research. The model we produced may also have use as a wildlife health planning tool to help managers identify health protection priorities and to promote actions across the determinants of health.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.093
GPT teacher head0.463
Teacher spread0.370 · 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.

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

Citations46
Published2018
Admission routes2
Has abstractyes

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