Interim Report of the Working Group on Small Pelagic Fishes, their Ecosystems and Climate Impact (WGSPEC)
Bibliographic record
Abstract
The Working Group on Small Pelagic Fishes, their Ecosystems and Climate Impact (WGSPEC) had its annual meeting in Plymouth, United Kingdom, 3–5 April 2017, which was attended by fourteen scientists and more than forty students from four different countries.The meeting started with an open-day event, during which the work done by WGSPEC and more in general the activities and opportunities provided by ICES were presented to students and early career scientists from Plymouth and from other UK and international universities. Attendees were engaged in a series of discussions around some key ques-tions/problems raised by the talks on fish-related issues and on a recommendation re-ceived by the joint ICES/OSPAR/HELCOM Working Group on Seabirds (JWGBird).The following two days were dedicated to discuss the progress done on ToR a) and ToR b). In particular for the ToR a): the work needed to complete the two manuscripts to be submitted at the special issue of Deep Sea Research was discussed in light of the feed-back received during the annual meeting in Victoria, Canada, and elsewhere. For the ToR b): preliminary comparison and analysis of the data of anchovy eggs and larvae col-lected in the Bay of Biscay, Catalan Sea and Ligurian Sea were shown and discussed
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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.041 |
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".