Theme Session L_Sustainable Aquaculture Impact Indicators.
Bibliographic record
Abstract
Book of abstracts of theme session L:Sustainable Aquaculture Impact IndicatorsConveners: Ellen S. Grefsrud (Norway), Dounia Hamoutene (Canada); Ann-Lisbeth Agnalt (Norway)Indicators in Crisis? CRISPR Salmon and the Tension Between Innovation and Environmental Governance in Norwegian aquaculture Dorothy Dankel The Effect of Adaptability of Nile Tilapia (Oreochromis niloticus) of Different Age Group on Growth, FCR, BCR, Survival and Water Quality Parameter in Biofloc Technology (BFT) EnvironmentEffects of Fish Farming Activities on Deep-Sea SpongesOffshore Low-Trophic Aquaculture Multi-Use Realisation (Olamur): Production Potential And Ecosystem Services Of Low Trophic Aquaculture At Multi-Use Areas In The Baltic and North Seas The Evolution of Aquaculture in ICES: A Historical Analysis of Research and Publications Monitoring the far-field ecological impact of open net-pen aquaculture in Icelandic fjordsHarmonizing Animal Health and Welfare in Modern Aquaculture: Innovative Practices for a Sustainable Seafood IndustryChemical inputs from the salmonid aquaculture industry and their impact on American and European lobsters: A review
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.132 |
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".