Ecological hypotheses for a historical reconstruction of upper trophic level biomass in the Baltic Sea and Skagerrak
Bibliographic record
Abstract
Fish and marine mammal populations in the Baltic Sea and Skagerrak have undergone major fluctuations over the past five centuries. We summarize how these fluctuations may have depended on various forms of predation (e.g., cannibalism, fishing, hunting) and environmental processes. The best-documented long-term fisheries in this region are the herring (Clupea harengus) fisheries near Bohuslän, western Sweden, and in the Øresund. These fisheries have been important since at least the 1200s and appear to be partly climatically driven. However, in the rest of the Baltic, information about fisheries for herring and other fish species is rare until after 1900. During the 20th century, while the Baltic underwent eutrophication, the biomass and landings of three fish species (cod (Gadus morhua), herring, and sprat (Sprattus sprattus)) all increased, whereas the biomass of marine mammals (grey seals (Halichoerus grypus), ringed seals (Phoca hispida), harbour porpoises (Phocoena phocoena)) decreased. The relative roles of exploitation, marine mammal predation, and environmental variability (e.g., eutrophication, major inflows of saline water, climate change) on the long-term dynamics of key fish species is not clear and requires increased collaboration among historians, fisheries and marine mammal ecologists, oceanographers, and climatologists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".