Theme Session C_Climate-Ready Fisheries Management in the UN Decade of Ocean Science for Sustainable Development
Bibliographic record
Abstract
Book of abstracts of theme session C:Climate-Ready Fisheries Management in the UN Decade of Ocean Science for Sustainable DevelopmentConveners: Steven Bograd (USA), Sanae Chiba (Canada), Kathy Mills (USA), Dave Reid (Denmark)Integrating Human Induced Fish Maturity Size Changes into Multispecies Models for Climate Ready Ecosystem Based Fisheries ManagementA method allowing getting insight about the adaptive potential to environmental changes in abundant exploited fish speciesMoving towards more holistic environment-informed stock assessments: insights from seven U.S. case studiesParticipatory qualitative network modelling to explore pathways for climate-ready fisheries in the Gulf of St-Lawrence, CanadaShould reference points change with changing productivity? Visualizing alternatives for Pacific herring stocksPlanning multi-dimensional approaches for climate-resilient marine fisheriesModeling the Future of Ocean Ecosystems in a Changing ClimatePerception of risks for German fisheries – an interdisciplinary scientific perspectiveExploring novel approaches to account for climate risk in setting catch adviceThe effects of seasonal variability on ecosystem-based management objectives: a case study of the Georges Bank Sea Scallop fishery
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.280 | 0.093 |
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".