Bibliographic record
Abstract
Director's foreword (Ussif Rasid Sumaila). Preliminary estimate of total marine fisheries catches in Corsica, France (1950-2008) (Frédéric Le Manach, Delphine Dura, Anthony Perec, Jean-Jacques Riutort, Pierre Lejeune, Marie-Catherine Santoni, Jean-Michel Culioli, and Daniel Pauly). A brief history of fishing in the Kerguelen Islands, France (M.L.D. Palomares and D. Pauly). Reconstruction of total marine fisheries catches for Madagascar (1950-2008) (Frédéric Le Manach, Charlotte Gough, Frances Humber, Sarah Harper, and Dirk Zeller). Reconstruction of marine fisheries catches for Mauritius and its outer islands, 1950-2008 (Lea Boistol, Sarah Harper, Shawn Booth and Dirk Zeller). Reconstruction of Nauru's fisheries catches: 1950-2008 (Pablo Trujillo, Sarah Harper and Dirk Zeller). Marine fisheries of Palau, 1950-2008: total reconstructed catch (Stephanie Lingard, Sarah Harper, Yoshi Ota and Dirk Zeller). Reconstruction of Sri Lanka's fisheries catches: 1950-2008 (Devon O‘Meara, Sarah Harper, Nishan Perera and Dirk Zeller). From local to global: a catch reconstruction of Taiwan's fisheries from 1950-2007 (Daniel Kuo and Shawn Booth). Reconstruction of fisheries catches for Tokelau (1950-2009) (Kyrstn Zylich, Sarah Harper, and Dirk Zeller). Reconstructing marine fisheries catches for the Kingdom of Tonga: 1950-2007 (Patricia Sun, Sarah Harper, Shawn Booth and Dirk Zeller). Reconstruction of marine fisheries catches for Tuvalu (1950-2009) (Kendyl Crawford, Sarah Harper and Dirk Zeller).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.016 |
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".