Total marine fisheries extractions by country in the Baltic Sea: 1950-present
Bibliographic record
Abstract
Director’s Foreword (Ussif Rashid Sumaila). Executive Summary (Sea Around Us). Fisheries catches from the Baltic Sea Large Marine Ecosystem: 1950-2007 (Dirk Zeller, Shawn Booth, Sarah Bale, Peter Rossing, Sarah Harper and Daniel Pauly). Denmark’s marine fisheries catches in the Baltic Sea (1950-2007) (Sarah Bale, Peter Rossing, Shawn Booth and Dirk Zeller). Catch reconstruction for Estonia in the Baltic Sea from 1950–2007 (Liane Veitch, Shawn Booth, Sarah Harper, Peter Rossing and Dirk Zeller). Baltic Sea fisheries catches for Finland (1950-2007) (Peter Rossing, Sarah Bale, Sarah Harper and Dirk Zeller). Germany’s marine fisheries catches in the Baltic Sea (1950-2007) (Peter Rossing, Cornelius Hammer, Sarah Bale, Sarah Harper, Shawn Booth and Dirk Zeller). Catch reconstruction for Latvia in the Baltic Sea from 1950-2007 (Peter Rossinga, Maris Plikshsb, Shawn Bootha, Liane Veitcha and Dirk Zeller). Catch reconstruction for Lithuania in the Baltic Sea from 1950-2007 (Liane Veitch, Sarunas Toliusis, Shawn Booth, Peter Rossing, Sarah Harper and Dirk Zeller). Poland’s fisheries catches in the Baltic Sea (1950-2007) (Sarah Balea, Peter Rossinga, Shawn Bootha, Pawel Wowkonowiczb and Dirk Zeller). Russian fisheries catches in the Baltic Sea from 1950-2007 (Sarah Harper, Sergey Shibaev, Olga Baryshnikova, Peter Rossing, Shawn Booth and Dirk Zeller). Sweden’s fisheries catches in the Baltic Sea (1950-2007) (Lo Persson).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".