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Record W2245467037 · doi:10.1093/conphys/cov059

Fisheries conservation on the high seas: linking conservation physiology and fisheries ecology for the management of large pelagic fishes

2016· article· en· W2245467037 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueConservation Physiology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Oceanic and Atmospheric AdministrationU.S. Department of CommerceNational Science Foundation
KeywordsBiologyPelagic zoneFisheryFisheries scienceFisheries managementEcologyFishing

Abstract

fetched live from OpenAlex

Populations of tunas, billfishes and pelagic sharks are fished at or over capacity in many regions of the world. They are captured by directed commercial and recreational fisheries (the latter of which often promote catch and release) or as incidental catch or bycatch in commercial fisheries. Population assessments of pelagic fishes typically incorporate catch-per-unit-effort time-series data from commercial and recreational fisheries; however, there have been notable changes in target species, areas fished and depth-specific gear deployments over the years that may have affected catchability. Some regional fisheries management organizations take into account the effects of time- and area-specific changes in the behaviours of fish and fishers, as well as fishing gear, to standardize catch-per-unit-effort indices and refine population estimates. However, estimates of changes in stock size over time may be very sensitive to underlying assumptions of the effects of oceanographic conditions and prey distribution on the horizontal and vertical movement patterns and distribution of pelagic fishes. Effective management and successful conservation of pelagic fishes requires a mechanistic understanding of their physiological and behavioural responses to environmental variability, potential for interaction with commercial and recreational fishing gear, and the capture process. The interdisciplinary field of conservation physiology can provide insights into pelagic fish demography and ecology (including environmental relationships and interspecific interactions) by uniting the complementary expertise and skills of fish physiologists and fisheries scientists. The iterative testing by one discipline of hypotheses generated by the other can span the fundamental-applied science continuum, leading to the development of robust insights supporting informed management. The resulting species-specific understanding of physiological abilities and tolerances can help to improve stock assessments, develop effective bycatch-reduction strategies, predict rates of post-release mortality, and forecast the population effects of environmental change. In this synthesis, we review several examples of these interdisciplinary collaborations that currently benefit pelagic fisheries management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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