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Record W6888009760 · doi:10.17895/ices.pub.8274

01 WGNARS - Interim Report of the Working Group on the Northwest Atlantic Regional Sea (WGNARS)

2018· article· en· W6888009760 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsNinthInterimWork (physics)Working groupBayGovernment (linguistics)Best practice

Abstract

fetched live from OpenAlex

The ninth meeting of the Working Group on the Northwest Atlantic Regional Sea (WGNARS), chaired by Robert Gregory (Canada) and Geret DePiper (USA) was held at the Waquoit Bay National Estuarine Research Reserve, in Falmouth, MA, USA, on 5–9 March 2018. The meeting was attended by 22 participants from the US and Canada, with an additional two participants calling in remotely. The overarching objective of WGNARS is to develop Integrated Ecosystem Assessment (IEA) capacity in the North-west Atlantic region to support ecosystem approaches to science and management.This report reviews the second WGNARS meeting within the current 3-year Terms of Reference (ToRs). The two major activities at the March 2018 meeting were to (a) pre-pare a progress report on ToRs a–e, and (b) encourage and enhance intersessional col-laboration across the region on research initiatives in support of IEA science. In 2017, highlights of this intersessional work include the 2017 collaboration between WGNARS and the US Mid-Atlantic Fishery Management Council on an ecosystem-level risk assessment, and member contributions towards the draft Canadian State of the Ocean (SOTO) Report for the Atlantic. During 2018, a number of WG products will be developed to continue to address the current ToRs including (1) a review of best practices in incorporating habitat into IEA science, (2) a linear transition matrix model for showcasing intertemporal management trade-offs, (3) comparisons of quantitative and qualitative models to assess the level of concurrence between the approaches, (4) preparation of a manuscript exploring the limitations of currently employed indicator analyses, (5) development of a dynamic factor analysis to facilitate indicator selection, (6) spatial approaches to indicator assessment, and (7) strawmen conceptual models to illustrate the transition from the Mid-Atlantic Fishery Management Council’s Risk As-sessment to an ecosystem-level management strategy evaluation.

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)0.014
metaresearch head score (Gemma)0.011
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: Other · Consensus signal: Other
Teacher disagreement score0.046
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0030.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0420.033

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 head0.045
GPT teacher head0.231
Teacher spread0.186 · 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
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
Published2018
Admission routes1
Has abstractyes

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