Theme session C - Operationalizing resilience for climate change impacts (Co-sponsored by PICES)
Bibliographic record
Abstract
ICES Annual Science Conference <strong>Book of abstracts of theme session C:</strong> <strong>Operationalizing resilience for climate change impacts (Co-sponsored by PICES)</strong> Conveners: Andrea Belgrano (Sweden), Keith Criddle (USA), Karen Hunter (Canada), Mitsutaku Makino (Japan), Luc Doyen (France), Iñigo Martinez (ICES), Julie Kellner (ICES) CM 21: A climate change risk assessment for fishing fleets and fishery-dependent coastal communities in Oman CM 67: Analysing global status and gaps of indicator-based climate change vulnerability assessments on marine fisheries social-ecological systems CM 75: Climate change impacts on Western Baltic cod and herring fisheries – an ecological-economic multispecies modeling approach CM 83: (Mal)adapting to climate change: a stylized fishery example CM 94: Organising the toolbox of climate change adaptations for small-scale fisheries CM 166: Climate change risk and adaptation in Ghana fisheries CM 170: Climate-proof management of North Sea cod (Gadus morhua L.) in a deeply uncertain future CM 207: Climate Hazard, Exposure and Vulnerability of the Swedish Fisheries CM 211: CUSPRA: a new method to assess ecological resilience to multiple interactive drivers CM 216: The effect of fisheries management, internal dynamics and climate change on the recovery of North Sea fish stocks CM 256: Identifying global hotspots for implementation of adaptive harvest allocation of shifting fish stocks CM 290: Resilience dynamics of Mediterranean demersal communities in response to climate change CM 293: Enhancing adaptive capacity assessment in fisheries decision making: Identifying barriers and ways to overcome them CM 308: Realizing tipping points: understanding causes and consequences of regime shifts in the European hake fishery CM 345: A simulation-based approach to assess the stability of marine food-webs and inform Good Environmental Status CM 350: Improving the climate change resiliency of commercial fishing ports in New Jersey, northeast U.S. CM 359: Climate resilience in marine fisheries: strategies for adapting to species distribution shifts CM 392: Velocity of climate change drives unexpected and resilient responses in species of the Western Mediterranean Sea CM 484: Resilience management for coastal fisheries facing with climate, demographical and oil price uncertainties CM 524: Food Web Modeling, Ecological Network Analysis and Simplified Bayesian Synthesis Combine to Inform System-Level Resilience Management in the Rapidly Warming Gulf of Maine CM 559: Operationalizing resilience for climate change impacts CM 563: Identifying policy pathways to build resilience in marine fisheries with differing capacities and contexts CM 565: Climate adaptability assessment tool for Canada's fisheries management
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".