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Record W6925334232 · doi:10.17895/ices.pub.19145546.v1

Working Group on the Ecosystem Approach to Ocean Health and Stressors: Mandates for Ecosystem-based Ocean Governance across Canada, the EU, and the US. March 2018, London UK

2022· report· en· W6925334232 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Council for the Exploration of the Sea (ICES) · 2022
Typereport
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
FundersEuropean Commission
KeywordsTask groupAllianceCorporate governanceTask (project management)Ecosystem-based managementEcosystemMarine ecosystemWorking group

Abstract

fetched live from OpenAlex

The Atlantic Ocean Research Alliance Working Group on Ecosystem Approach to Ocean Health and Stressors formed a task group to explore the mandate/s for ecosystem based management (EBM) in the North Atlantic. The task group met 13-16 March 2018 in London and was comprised of an interdisciplinary mix of legal, political, administrative, and natural scientists/scholars from the three jurisdictions that signed the Galway Statement on Atlantic Ocean Cooperation (US, Canada, and EU). The task group workshop identified the major mandates that govern marine activities and the stressors that impact ocean health and condition. The overarching goal was to characterize, compare, and synthesize the mandates that govern marine activities and ocean stressors relative to facilitating EBM in the North Atlantic (national and international waters). The group also identified impediments to the incorporation of science into the management process, largely based on a cross-comparison of jurisdictional applications of mandates and highlighted benefits of improved implementation of existing mandates for EBM.

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)0.017
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0070.003
Open science0.0030.007
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0190.005

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 head0.137
GPT teacher head0.302
Teacher spread0.165 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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