Harvesting krill : ecological impact, assessment, products & markets
Bibliographic record
Abstract
Director's lntroduction (Tony Pitcher) -- CONTRIBUTED PAPERS : Modelling and data requirements for management of the Antarctic krill-based ecosystem (David Agnew) -- Fishery biology of Euphausia pacifica in the Japanese\nwaters (Yoshinari Endo) -- The CCAMLR experience (Inigo Everson) -- \nPutting krill into ecosystem models (Astrid Jarre-Teichmann) -- Acoustic estimation of krill abundance utilizing volume scattering\nmeasurements (Michael C. Macaulay) -- Exploitation and management of Antarctic krill (Denzil G.M. Miller and David J. Agnew) -- Bycatch of juvenile fishes the Antarctic krill fishery (Carlos A. Moreno) Development of the krill fishing industry (Stephen Nicol) Biological information necessary for the management of the Antarctic krill\nfishery Stephen Nicol -- Using the Ecopath II approach to assess krill biomass and dynamics (Daniel Pauly) -- Comparisons of repeat acoustic surveys in Jervis Inlet, British Columbia, 1994-\n1995 (S.J. Romaine et al.) -- Overview of the krill of the Gulf of St.Lawrence (Yvan Simard) -- The importance of euphausiids to a British Columbian coastal marine\necosystem (R W. Tanasichuk) -- The influence of inter-annual variations in sea temperature on the population biology and productivity of euphausiids in Barkley Sound, Canada (R W. Tanasichuk) -- DISCUSSION PAPERS -- Three components leading to an assessment of the potential yield of British\nColumbia krill stocks: a fishery manager's point of view (Jim Morrison) -- \nSummary of workshop discussion (addressing issues raised by Jim\nMorrison) (Taja Lee) -- List of participants Workshop agenda
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".