Theme Session F_Improving operational implementation of spatial stock assessment and management: harnessing novel approaches and data to overcome spatial alignment challenges
Bibliographic record
Abstract
Book of abstracts of theme session F:Improving operational implementation of spatial stock assessment and management: harnessing novel approaches and data to overcome spatial alignment challengesConveners: Georgios Kerametsidis (Spain), Aaron Berger (USA), Manuel Hidalgo (Spain), Patrick Lynch (USA)Integrating Deep Learning for Fish Size Estimation and Vessel Tracking in Spatial Fisheries ManagementDetermining and Managing Atlantic Tarpon Stocks Amid Spatial Mismatch, Entrainment Mechanisms, and CollapseApplication of a spatially-explicit management strategy evaluation tool for testing performance of stock assessmentsOpportunities, needs, and barriers towards the operational implementation of spatial stock assessment and management: session overview and expectationsUnderstanding Potential Biases in Non-spatial Stock Assessments: A Spatially Explicit Simulation-Estimation Experiment for the Northwestern Mediterranean SeaParticle-tracking models and otolith microchemistry reveal the dynamics of the intra-stock connectivity of four-spot megrim in the north Iberian Peninsula Spawning migration of the European eel following a local fishery closure Modelling spawning dynamics and larval dispersal of Patagonian toothfish (Dissostichus eleginoides) in Kerguelen waters‘Drivin’ with Your Eyes Closed’: Results from an International, Blinded Simulation Experiment to Evaluate Spatial Stock AssessmentsAdvancing 3D otolith shape analysis for stock identificationPanmictic Panacea or Spatial Necessity? Demonstrating Good Practices for Developing Spatial Stock Assessments through Application to Alaska Sablefish (Anoplopoma fimbria)Sandeel in space - Fisheries displacement and management of North Sea sandeels in a post Brexit eraFrankenstein Species Distribution Modelling: A Composite Bayesian Spatial Framework Integrating Ecoregion-Specific Spatial Structures to Account for Environmental HeterogeneityDo changes in the spatial distribution of stocks drive fleet dynamics? The case of European hake in the northern Spanish waters Inferring spatio-temporal spawning patterns using fishery-dependent data: the case of European hake An integrative approach: Computing isoscapes from the biological community on the Spanish Mediterranean coast to inform spatial managementMixed but not messy! Joining aspects of mixed fisheries to inform stock assessment and management Mapping a Network of Collaboration Between Vessels in the Tuna Purse Seine FisheryMovement patterns and habitat use of Greenland halibut (Reinhardtius hippoglossoides) in Newfoundland and Labrador, CanadaABISS: Automated Benthic Imaging Software Suite to assist stock and biodiversity assessments of benthosWhose line is it, anyway? Testing the impact of misalignment in biology, assessment, and management Impacts of considering age-based spatial stock structure on Pacific Sardine (Sardinops sagax) management in the California Current EcosystemTowards integrating spatial structure and connectivity patterns in European hake (Merluccius merluccius) stock assessmentStability and drivers of Atlantic cod subpopulations in the North Sea Identifying risk pathways for highly migratory species in the Mediterranean Sea
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.060 |
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".