Bibliographic record
Abstract
Turning the tide is easy. Tides are after all very predictable; just wait for the right moment before pushing the water back. When it comes to re-directing a current it is far more difficult – it takes climate change to shift the Gulf Stream. What is happening to the world's fisheries, at the local, regional, or global scale appears to be more like a one-way current than a tide with ups and downs (Pauly et al ., 2003). Strong enforcement of effort restrictions may bring a relief in the parts of the world where strong governance is in place (Worm et al ., 2009), while most of the world's marine ecosystems continue to be overexploited. We are gradually eroding many of the ecosystems on which our food supply from the oceans relies, even if we may not notice it as individuals (Pauly, 1995). What can we do to curb the direction of widespread degradation? It is a daunting task to embark on – one where we cannot explicitly express how we will go about solving the problem. We do, however, have an idea of, and experience with, techniques and materials we can use to deliver a small contribution toward the solution. What is clear is that if we as scientists are to make such contributions we must speak up and seek to be heard (Baron, this volume). We must convey the best available scientific information to decision and policymakers (Reichert, this volume).
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.015 |
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".