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Record W4411948200 · doi:10.24908/ohi.v3i1.17809

Applying a One Health Lens to Mitigating Vehicular Impacts on Marine Mammals

2025· article· en· W4411948200 on OpenAlex
Mackenzie Doucett-Forget, Janna Andre, Cheryl Spurvey

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOne Health Innovation · 2025
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsLens (geology)Environmental scienceThrough-the-lens meteringOpticsPhysics

Abstract

fetched live from OpenAlex

Marine vehicles play an important role in Newfoundland and Labrador’s (NL) economy, transportation, and essential services. However, they also pose significant threats to humans, marine mammals, and the environment via collisions, noise and/or light pollution, habitat destruction, emissions, and water contamination. These impacts are deeply interconnected, contributing to a complex and evolving wicked problem. Conservation efforts in NL, including grassroots initiatives and government regulations, reflect strong community interest but often lack enforcement mechanisms, long-term support, and measurable outcomes. Applying the One Health perspective highlights the value of community-informed, interdisciplinary approaches that address the complex and overlapping impacts of marine traffic, guiding the development of sustainable solutions for humans, marine mammals, and the environment.

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)0.002
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: none
Teacher disagreement score0.810
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.373
Teacher spread0.310 · 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