Cold-water shellfish as harvestable resources and important ecosystem players
Bibliographic record
Abstract
Abstract The symposium, Shellfish—Resources and Invaders of the North, took place 5–7 November 2019 in Tromsø, Norway. Approximately 110 participants presented and discussed 60 talks and 25 posters. Of these, ten articles are published in this symposium issue. The goal of this symposium was to discuss the role of shellfish, both as harvestable resources and as important ecosystem players in northern hemisphere cold marine environments. To provide perspective for the symposium, the development of four major crustacean fisheries (northern shrimp, snow crab, Homarus, Norway lobster) are reviewed. Our review showed that landings of all these fisheries are still in a state of flux due to inherent population dynamics, fishing, and climate change. The talks and posters covered a broad range of state-of-the-art bioecological knowledge and present challenges in the assessment and management of the most ecologically and/or commercially important cold-water shellfish species belonging to the phyla Arthropoda, Mollusca, and Echinodermata. Various alternative harvesting and management techniques were presented along with perspectives for shellfish aquaculture. Methods and models for stock assessment were thoroughly covered as well as the ecological role of shellfish, their population dynamics, new insights into their biology and genetics, and their changing distribution and significance as invasive species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".