Final Report of the Fifth Meeting of Scientific Experts on Fish Stocks in the Central Arctic Ocean
Bibliographic record
Abstract
This report provides a summary of the 5th meeting of scientific experts on Fish Stocks in the Central \nArctic Ocean (FiSCAO) on October 24‐26, 2017, in Ottawa, Canada. \nAt the request of the 10 parties negotiating on an agreement to prevent unregulated commercial fishing \nin the High Seas portion of the Central Arctic Ocean (CAO), participants of the 5th FiSCAO meeting were \ntasked with addressing four Terms of Reference, summarized below: \n ToR 1. Design a 1‐3 year long mapping program. \n ToR 2. Design a monitoring program. \n ToR 3. Identify human, financial, vessel/equipment resources needed for mapping and \nmonitoring. \n ToR 4. Develop data collection, sharing, and hosting protocols that outline the details of what \nand how data shall be collected, shared, and hosted for consideration by the Parties. \nThe 5th FiSCAO meeting included scientific representatives from seven states including Canada, the \nPeople's Republic of China, the European Union, Iceland, the Republic of Korea, the Kingdom of Norway \nand the United States of America. The meeting also included representatives from the International \nCouncil for the Exploration of the Sea (ICES), the North Pacific Marine Science Organization (PICES) and \nthe Arctic Council’s Protection of the Arctic Marine Environment (PAME) and Conservation of Arctic \nFlora and Fauna (CAFF) working groups. \nThe report summarizes the elements for collecting baseline data (i.e., a mapping program) in the high \nseas CAO to achieve the goals of documenting species distributions, relative abundances and key \necosystem parameters (ToR 1). The mapping program describes the priority areas to sample, the types \nof data to collect and possible data collection approaches to employ. Participants emphasized that \nexisting planned surveys are very limited, and that significant dedicated resources will be required to \nimplement the mapping program. \nThe report outlines a strategy for monitoring indicators of fish stocks and ecosystem components (ToR \n2). The report includes a list of existing monitoring programs and a prioritized list of indicators to detect \nenvironmental change in the high seas CAO. Further refinement of a monitoring program will use \ninformation from the mapping program (ToR 1). Participants emphasized the need to begin monitoring \nas soon as possible and that additional research is required to operationalize monitoring indicators. \nThe report summarizes the preliminary cost estimates (ToR 3) to implement a mapping program to \ncollect data in the high seas portion of the CAO using a vessel of opportunity and in the Pacific Gateway \nregion of the CAO using an independently‐organized survey. Cost implications for the monitoring \nprogram and other scientific activities are also listed (e.g., data analysis, data management). \nThe report includes a draft data sharing policy as the foundation for a future data sharing protocol, \nincluding the technical specifications for data sharing (ToR 4). The development of the data sharing \nprotocol will require negotiation and legal review among the participating states. A data management \nand data sharing pilot study on a CAO fish database is suggested to test a framework.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.017 |
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".