Theme Session I – Towards an improved global fisheries management through genomic solutions
Bibliographic record
Abstract
Book of abstracts of theme session I:Towards an improved global fisheries management through genomic solutionsConveners: Naiara Rodríguez-Ezpeleta (Spain), Ian Bradbury (Canada)CM 31: Exploring differences between inshore and offshore stocks of Greenland halibut by population genomicsCM 51: Integration of genomic and ecologic methods inform management of an undescribed, yet highly exploited, sardine speciesCM 73: Assessing spatial resolution of low-coverage whole-genome sequencing to refine management units of a commercial bivalveCM 146: Can eDNA produce stock abundance indices for small pelagics?CM 215: The (in)complete diet of Northeast Atlantic mackerel (Scomber scombrus) during its summer migration into Icelandic waters, using DNA barcoding and visual analysisCM 228: Genomic-based predictions of climate change impacts in aquatic species and the GenARCC projectCM 243: Development of Close-Kin Mark-Recapture model for Scotian Shelf Atlantic HalibutCM 254: Using eDNA metabarcoding to characterize freshwater fish community composition and climate change impacts in Northern CanadaCM 255: Northern Cod temporal and spatial dynamics: integrating genomics with telemetry dataCM 311: Close-Kin Mark-Recapture for abundance estimation of the European hake and the white anglerfishCM 367: Genomic analysis of levels and patterns of genetic diversity in populations of european hakeCM 402: Chromosome-level genome assembly of the European anchovy: A valuable resource for adaptation and conservation studiesCM 466: Implementation of a Genetic-informed Assessment for European Hake (Merluccius merluccius)CM 550: Assessing the potential of environmental DNA for quantitative monitoring of coastal fish populationsCM 660: Genomic population structure of Littorina littorea as revelled by data from ddRAD sequencing
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.222 | 0.100 |
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".