Working Group on American Eel (WGAMEEL; outputs from 2022 meeting)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Working Group on American Eel (WGAMEEL) met remotely in September 2022 to initiate work on the first three of five Terms of Reference (TORs) of its three-year fixed term. The first two TORs tasked WGAMEEL with listing and evaluating data on American eel landings, abundance indices, and spatial and habitat data and also to describe assessment methods used in Canada and the United States (US). The third TOR was to consider Indigenous Knowledge Systems for American eel. American eel commercial landings data was compiled from the 1870s through recent years. Recent reports in both countries have identified abundance data sets that are suitable for use in stock assessments based on criteria for evaluating data sets developed by both countries. In Canada, 37 data series were reviewed, of which 16 were determined to be suitable for use in stock assessments. In the US, over 80 fishery-independent surveys from the US Atlantic coast were reviewed for the development of 25 young of the year, 10 elver, and 14 yellow eel relative abundance indices. Overall, abundance indices in both countries indicate a decline in eel abundance, although declining trends were not found in all surveyed areas. All of the series evaluated for trends are from the St. Lawrence Basin, the Canadian Maritime Provinces, and the US Atlantic coast. No abundance series are available for eel range outside these areas. WGAMEEL noted a deficiency of long time series, incomplete sampling across the range, data sharing issues, and informal or opportunistic data collection in some areas. There are commonalities in some of the US and Canadian data available for spatial modelling of American eel. A larger challenge for spatial modelling in the US and Canada will be acquiring enough high quality, georeferenced biological data sets with suitable observations to assess occurrence, abundance, and trends over time in a spatial framework. WGAMEEL also began work to consider Indigenous Knowledge Systems and their distinct way of knowing through its four orientations. WGAMEEL will continue over the next two years to address the third TOR as well as the remaining two on stock assessment methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it