Management objectives, trade-offs and strategies in a changing ocean
Bibliographic record
Abstract
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.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".