Future-oriented Seafood Markets: Economic dimensions, ecological compatibility and social aspects of fisheries and aquacultures
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Converners: Cornelia Kreiss (Germany), Rasmus Nielsen (Denmark). CM 2018/L:34. Being in the stranglehold of conservationists. Stakeholders perceptions on the stagnating German aquaculture production. Antje Gimpel, Tobias LasnerCM 2018/L:643. Moving to a new target group? conclusions from comprehensive consumer studies of the PrimeFish project for the seafood industry. Matullat I., Mora C., Menozzi D., Sogari G., Nguyen T.T., Hagen B., Carcagnì A., Cubero Dudinskaya E., Guido D., Untilov O., Ganassali S.CM 2018/L:90. Sustainability knowledge to sustainable fish choice: consumer based insights from emerging markets. Mohammed Ziaul Hoque, Jinghua Xie, Ph.D, Øystein Myrland, Ph.DCM 2018/L:512. Fishing gears’ effect on ex-vessel fish prices. Tim Cashion, U. Rashid SumailaCM 2018/L:205. The potential of small scale and traditional firms in fisheries and aquaculture. Daurès Fabienne, Girard Sophie, Lasner Tobias, Goti LeyreCM 2018/L:585. The theory of change underpinning seafood ecolabelling tested through stock assessments. Catherine S. Longo, Ashleigh Arton, Annette Scheffer, Francis NeatCM 2018/L:419. Consumer preference heterogeneity and preference segmentation: the case of eco-labeled salmon in Danish retail sales. Isaac Ankamah-Yeboah, Frank Asche, Julia Bronnmann, Max Nielsen, Rasmus NielsenCM 2018/L:409. Is whitefish just white? a comparison of production costs and substitution of wild and farmed whitefish. Cornelia Kreiss, Rasmus Nielsen, Giap V. Nguyen, Sarah Simons, Tobias LasnerCM 2018/L:287. The young consumers of fish products –the determinants of purchasing processes. Adam Mytlewski, Tomasz KulikowskiCM 2018/L:360. Transnational localism: market practices of small scale fishers in the Mediterranean. Jerneja PencaCM 2018/L:462. Fisheries eco-certification: a large-scale societal experiment? Kochalski, SCM 2018/L:93. What should be recognized and rewarded in sustainable fisheries designations? fish harvester perspectives and preferences in Newfoundland and Labrador. Courtenay E. Parlee, Paul Foley, Erin CarruthersCM 2018/L:262. Teleconnection of cod stocks in the world market. Camilla Sguotti, Christian Möllmann, Andries RichterCM 2018/L:365. Estimating the impacts of common fisheries policy implementation to North Sea demersal fisheries using a bioeconomic mixed fisheries model. Marc H. Taylor, Alexander Kempf, Thomas Brunel, Clara Ulrich, Youen Vermard, Dorleta GarciaCM 2018/L:321. Game theory applications to Baltic Sea multispecies and multi-fleet fisheries under climate variability. Sezgin Tunca, Martin Lindegren, Lars-Ravn Jonsen, Marko Lindroos
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".