Integrating information on population structure and migration into fisheries stock assessment and management
Bibliographic record
Abstract
Conveners: Michael Frisk (USA), Lisa Kerr (USA), Matthew Siskey (USA), Daniel Duplisea (Canada).No abstracts are to be cited without prior reference to the author.Evaluating the consequences of misdiagnosing population structure within spatial stock assessment models: Katelyn M. Bosley, Amy Schueller, Aaron M. Berger, Jonathan Deroba, Daniel R. Goethel, Kari H. Fenske, Dana Hanselman, Brian Langseth.Where do you think you’re going? Improving the movement dynamics of spatial stock assessment models: Daniel R. Goethel, Katelyn M. Bosley, Aaron M. Berger, Jonathan Deroba, Kari H. Fenske, Dana Hanselman, Brian Langseth, Amy Schueller.Informing stock assessment with historical and current growth estimates of Eastern Baltic cod incorporating genetics and movement patterns from tagging data: Monica Mion, Alessandro Orio, Roman Motyka, Annelie Hilvarsson, Esha Mohamed, Krzysztof Radtke, Karin Hüssy, Maria Krüger-Johnsen, Kate McQueen, Stefanie Haase, Uwe Krumme, Maris Plikshs, Michele Casini.Implications of incorporating spatial population structure into a winter flounder stock assessment model: Matt Siskey, Lisa Kerr, Mike Frisk, Chantel Wetzel.A spatially explicit individual-based model to support management of commercial and recreational fisheries for European sea bass Dicentrarchus labrax: Nicola Walker, Robin Boyd, Joseph Watson, Zachary Radford, Lisa Readdy, Richard Sibly, Shovonlal Roy, Kieran Hyder.Immigration and cod (Gadus morhua) recovery: contrasting ecological and evolutionary rescue: Silva Uusi-Heikkilä, Anna Kuparinen.The dilemma of recovering populations within collapsed fish stocks: Peter Wright, Andrew Davie, Thomas Régnier , Alice Doyle, Fiona Gibb.Ignoring spawner spatial distribution patterns generates bias in stock-recruitment models: Stefan Skoglund, Kjell Leonardsson, Rebecca Whitlock, Stefan Palm, Erik Petersson.Estimating movement rates of bigeye tuna in the eastern Pacific Ocean for a spatially-structured stock assessment model based on archival tagging data: Haikun Xu, Mark Maunder, Alexandre Aires-da-Silva, Kurt Schaefer, Dan Fuller.Successes and challenges with multi-area stock assessment models: Richard D. Methot.Integrating ontogenetic movements into stock assessment: a spatial approach to investigate their relationship with fisheries exploitation pattern: Tommaso Russo, L. D’Andrea, S. Franceschini, D.M. Canu, A. Cucco, G. Garofalo, G. Quattrocchi, M. Sinerchia, C. Solidoro, F. Fiorentino.Testing spatial heterogeneity with stock assessment models: Ernesto Jardim, Margit Eero, Alexandra Silva, Clara Ulrich, Lionel Pawlowski, Steven J. Holmes, Leire Ibaibarriaga, Jose’ A.A. De Oliveira, Isabel Riveiro, Nekane Alzorriz, Leire Citores, Finlay Scott, Andres Uriarte, Pablo Carrera, Erwan Duhamel, Iago Mosqueira.Origin of Atlantic bluefin tuna in the U.S. rod and reel fishery and application of mixed stock information to fisheries management: Lisa Kerr, Zachary Whitener, Steve Cadrin, Molly Morse, David Secor, Walt Golet.Spatial stock assessment of anchovy (Engraulis ringens) off Central Southern Chile using the “areas-as-fleets” approach: María-José Zúñiga, Juan Valero Maite Pons.Combining genetic data and habitat modelling to map two flounder species in the Baltic Sea: Ann-Britt Florin, Lovisa Wennerström, Alessandro Orio, Stefan Palm, Anders Nissling, Linda Söderberg, Didzis Ustups, Karin Hüssy, Redik Eschbaum, Krzysztof Radtke, Michele Casini, Karin Limburg, Franziska Schade, Tore Prestegaard.Effects of spatial scale and seasonal fishery heterogeneity on stock assessment of Atlantic Sheepshead (Archosargus probatocephalus): Thom D. Teears, Jie Cao Jeffrey A. Buckel.Integrating genetic assignment of natal origin into Atlantic bluefin tuna Management Strategy Evaluation: Naiara Rodriguez-Ezpeleta, Haritz Arrizabalaga, Deirdre Brophy, Igaratza Fraile.The Close-Kin Mark Recapture method for North Atlantic fisheries: Ilaria Coscia, Jann Martinsohn, Richard Hillary, Antonella Zanzi, Naiara Rodriguez-Ezpeleta, Mark Bravington.Improving the use of data from tag recaptures in Stock Synthesis: Gavin Fay, Ashleigh J. Novak, Ian G. Taylor.Identification of cross boundary management units of River lamprey Lampetra fluvialtilis in Lithuania and Latvia: R. Staponkus, A. Samuilovienė.Creating and validating an individual-based model of shoaling behaviour to improve population estimates: Sophia Wassermann, Mark Johnson.Using otolith shape analysis and machine learning techniques in stock assessment and management of Atlantic herring: Florian Berg, Susan Mærsk Lusseau, Valerio Bartolino, Tomas Gröhsler, Cecilie Kvamme, Richard D. M. Nash, Aril Slotte.Incorporating migration and shared spawning with spatially explicit stock assessments for the European sea bass (Dicentrarchus labrax): Gwladys Lambert, Lisa Readdy, Kieran Hyder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".