Working Group on the Northwest Atlantic Regional Sea (WGNARS)
Bibliographic record
Abstract
The Working Group on the Northwest Atlantic Regional Sea (WGNARS) aims to develop the Integrated Ecosystem Assessment (IEA) capacity in the Northwest Atlantic region to support ecosystem approaches to science and management. IEAs are an iterative process that incorporate all aspects of an ecosystem, including humans, during the decision-making process to better ad-dress trade-offs within and between sectors. The working group has developed a process for assessing and communicating indicators that has been incorporated into the State of the Ecosys-tem reports produced annually in the United States. The same suite of indicators have been used in an ecological risk assessment of the Mid-Atlantic Bight for the Mid-Atlantic Fisheries Manage-ment Council. The working group continues to develop alternative models that represent the marine ecology and human systems at multiple scales. The working group’s annual meeting highlighted the work that has been accomplished through an open-science symposium that ad-dressed the usefulness of the IEA approach as well as what needs to be done to move forward with an ecosystem approach to management. In the coming years, the working group will con-tinue to focus on developing the capacity for IEAs within the United States and Canada. Em-phasis will be placed on utilizing the advantages of open science principles to gain efficiency in product development as management requests are often faster than scientific processes. The working group will continue to emphasize the value of incorporating human dimensions and improving communication. The main goal will be to expand the scope of IEAs beyond fisheries and include another sector such as wind energy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".