MétaCan
Menu
Back to cohort
Record W4301711479 · doi:10.47886/9781934874301.ch5

Advancing an Ecosystem Approach in the Gulf of Maine

2012· book-chapter· en· W4301711479 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2012
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsGroundfishFishingContext (archaeology)FisheryResource (disambiguation)Natural resourceFishing industryEcosystemBusinessMarine ecosystemFisheries managementEnvironmental resource managementCommercial fishingGeographyEnvironmental sciencePolitical scienceEcologyComputer science

Abstract

fetched live from OpenAlex

<i>Abstract</i>.—The Gulf of Maine (GoM) may have defined borders to some, but to the Canadian fishing industry, it carries a flow of larvae, nutrients, and other resources that help sustain the fishery from Georges Bank to the West Scotian Slope to the Bay of Fundy and all points in between. The GoM provides a source of wealth to people and communities, as well as supplying what may be one of the last natural foods on the planet. The fishing industry has been using the GoM for centuries, yet it is only recently that monitoring and data gathering has been taking place. In my opinion, we can extract much more value from the fisheries than we presently do. If the fisheries resource of the GoM is not delivering its full potential, who is ultimately responsible and accountable? In the past decade, transboundary groundfish resources from Georges Bank have been successfully managed through the Transboundary Management Guidance Committee. We can improve decision making even further in a greater ecosystem context, recognizing that decisions have to be made with the information available. An ecosystem approach to fisheries proposes a pragmatic view based on assessing the risk of not meeting agreed objectives.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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.

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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Fisheries Society eBooksSame topicMarine and fisheries researchFrench-language works237,207