Report of the Workshop on the Development of a Spatial Database and Model for Eels (WKSMEEL)
Bibliographic record
Abstract
WKSMEEL is part of the roadmap defined by the WKFEA (Workshop on the future of eel advice). Experts from Japan, USA, Canada and Europe met to discuss modelling methods and the data necessary to support the development of a spatial assessment of the stock of European eel (Anguilla anguilla). Modelling of the European eel stock is envisioned as a two-level modelling process. At the regional level, it requires the implementation of statistical extrapolation models in freshwater habitats and specific models in lakes or lagoons. At the whole-stock level, a stage-based spatial model is envisioned. The rationale for choosing a stage-based approach for the global models and the requirements and validation of different regional models are discussed.The data requirements for both regional and global stock models have been reviewed with a specific focus on spatial data. For the regional models, a common GIS data structure using broad scale river networks or national databases is proposed. This database should contain both water surface (lake, lagoons, transitional waters) and rivers. A database structure to store information on eel habitat, dam and electrofishing is proposed. The availability of GIS river database, dam data and electrofishing data is assessed at the European level, using survey questionnaires sent to national correspondents. For the global stock model, the data requirement, including the output of the regional models, have been defined.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".