Management objectives, trade-offs and strategies in a changing ocean
Notice bibliographique
Résumé
Conveners: Jamie Tam (Canada), Lisa Kerr (USA), Robert Thorpe (UK).No abstracts are to be cited without prior reference to the author.Exploring parameter uncertainty and production in sub-Arctic and Arctic ecosystems: Jamie C. Tam, Alida Bundy, Torstein Pedersen, Nina Mikkelsen.Combining ecosystem models to define multi-species reference points: Michael Spence, Hayley J. Bannister, Robert B. Thorpe, Nicola J. Walker.Are data-limited methods sustainable approaches? Management strategy evaluation of data-limited methods with a data-rich stock: Ming Sun, Yunzhou Li, Yiping Ren, Yong Chen.Tradeoffs and uncertainty in herring fisheries management: insights from management strategy evaluation and ecosystem modellings: Szymon Surma.Progress towards evaluating fisheries management strategies with ecosystem modelling tools: Holly Perryman, Cecile Hansen, Daniel Howell, Daisuke Goto, Erik Olsen.Aligning science and policy in the Humboldt Current to achieve climate-ready fisheries management: Kristin M. Kleisner, Merrick Burden, Erica Cunningham, Mauricio Galvez, Renato Guevara, Dimitri Gutierrez, Jaime Letelier, Carlos Montenegro, Miguel Ñiquen, Vincent Saba, Jorge Tam.Integrating climate change vulnerability indices on a scale relevant to fishery managers: Nancy L. Shackell, , Blair J. W. Greenan, Kiyomi French, Phil Greyson, Andrew Cogswell, David Brickman, Zeliang Wang, Vincent S. Saba.The potential of EU fisheries to contribute to sustainable food and nutrition security: Friederike Ziegler, Louisa Borthwick, Marta Angela Bianchi, Sara Hornborg, Ollie Van Hal, Hannah H.E. Van Zanten.The global rise of crustaceans: shelling out more for seafood?: Robert Boenish, Jake Kritzer, Kristin Kleisner, Robert Steneck, Jose Ingles, Wenbin Zhu, Frederick Schram, Douglas Rader, William Cheung, Karl Michael-Werner, Yongjun Tian, John Mimikakis.Predicting the effect of fishing on indicators of good environmental status in the North Sea: an ensemble modelling approach: Christopher A. Griffiths, Christopher Lynam, Hayley Bannister, James Waggitt, Robert Thorpe, Michael Spence.A modular framework for the generic application of fisheries management strategy evaluation: Ernesto Jardim, Finlay Scott, Paris Vasilakopoulos, Cecilia Pinto, Alessandro Mannini, Christoph Konrad, Iago Mosqueira.Rebuilding plan for western Baltic herring. Can we use simple MSE-type forecasts?: Vanessa Trijoulet, Casper W. Berg, Claus R. Sparrevohn, Anders Nielsen, Henrik Mosegaard.Evaluating the performance of management strategies for Northeast US groundfish fisheries in a changing climate: Samuel Truesdell, Lisa Kerr, Steven Cadrin, Jonathan Cummings, Gavin Fay, Sarah Gaichas, Andrew Pershing.The challenges for ICES raised by conducting full MSEs for some jointly-managed stocks in the North Sea: De Oliveira, J.A.A., Fischer, S.H., Berges, B., Cole, H.S., Devine, J.A., Goto, D., Hintzen, N.T., Miethe, T., Mosqueira, I., Umar, I., Walker, N.D., Jardim, E.Spaced out: Investigating the impact of spatial structure and movement under climate change using management strategy evaluation: Nis Sand Jacobsen, Kristin Marshall, Aaron Berger, Ian Taylor.Eliciting values and negotiating trade-offs in participatory management strategy evaluation: Mimi E. Lam, Tony J. Pitcher.Forage fish fisheries management requires a tailored approach to balance tradeoffs: Margaret C. Siple, Timothy E. Essington, Éva E. Plagányi.Conflicting goals and best strategies for reaching Good Environmental Status in the Baltic Sea: Kristina Heidrich, Christian Möllmann, Saskia Otto.Managing conflicts and synergies of use-use interactions: Ida Maria Bonnevie, Henning Sten Hansen, Lise Schrøder.Assessment of alternative management procedures for the US recreational summer flounder fishery: Jason E. McNamee, Amanda R. Hart, Gavin Fay, Kiley J. Dancy.Addressing the impact of climate change on “choke” species issues in a multispecies fishery: Lisa Kerr, Sam Truesdell, Gavin Fay, Jonathan Cummings, Ashley Weston, Steven X. Cadrin, Sarah Gaichas, Min-Yang Lee, Anna Birkenbach, Andrew Pershing.Building climate readiness into two different fishery systems: Merrick J. Burden, Kristin Kleisner, Alice Thomas-Smith, Kendra Karr, Larry Epstein, Erica Cunningham, Willow Battista, Rod Fujita.’Floundering in the face of climate change: Can we successfully manage our recreational fisheries without all the facts?: Amanda R. Hart, Gavin Fay, Jason McNamee, Kiley J. Dancy.Avoiding the curse of circularity: building a multi-species model from the ground up: Michael. A. Spence, Robert B. Thorpe, Paul G. Blackwell, Finlay Scott, Richard Southwell, Julia L. Blanchard.Use of management strategy evaluation to inform in fisheries management in a changing climate: Jonathan W. Cummings, Amanda Hart, Gavin Fay.Interactions in a fishery socio-ecological system revealed by a Bayesian Belief Network approach: Silvia de Juan, Andres Ospina-Alvarez, Montserrat Demestre, Francesc Mayno.What are process errors in population dynamic models and how do they relate to time-varying parameters?: Paula Silvar Viladomiu, Cóilín Minto, Deirdre Brophy, David G. Reid.Recruitment predictions we can make, and their significance under changing climate: Julie M. Gross, Philip Sadler, John M. Hoenig.Models for adaptive management in the face of climate change: Richard J. Bell, Jay Odell.Challenges and opportunities of ecosystem services integration into marine spatial planning: examples from case studies in the Baltic Sea: Solvita Strake, von Thenen M., Luhtala H., Schiele K., Hansen H. S.Risk avoidance—MSE collaboration for Bering Sea Tanner crab: Madison Shipley, William Stockhausen, Ben Daly, André Punt.Ecologically Sustainable Exploitation Rates – a multispecies approach for fisheries management: Torbjörn Säterberg, Michele Casini, Anna Gårdmark.
Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.
Comment cette classification a été obtenuedéplier
Le tri à trois modèles
les 5 600 travaux triés →Les trois modèles l'ont jugé hors champ.
Conference session abstract collection on fisheries management objectives, trade-offs, and strategies; the object is ocean and fisheries management, and the evaluation of stock assessment methods within it is domain methodology rather than a study of research practice.
The collection concerns fisheries management strategies and ecosystems, not research itself.
Fisheries management conference session abstracts; domain management science, not metaresearch.
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».