Deep antiquity of seagrasses supporting European eel fisheries in the western Baltic
Bibliographic record
Abstract
Protecting ocean habitats is critical for international efforts to mitigate climate impacts and ensure food security, but the ecological data upon which policy makers base conservation and restoration targets often reflect ecosystems that have already been deeply impacted by anthropogenic change. The archaeological record is a biomolecular archive offering a temporal scope that cannot be gathered from historical records or contemporary fieldwork. Insights from biogeochemical and osteometric analyses of fish bones, combined with context from contemporary field studies, show how prehistoric fisheries in the western Baltic relied on seagrass meadows. European eels ( Anguilla anguilla ) harvested by Mesolithic and Neolithic peoples over millennia showed a strong fidelity for eelgrass foraging habitats, an ecological relationship that remains largely overlooked today, demonstrating the value of protecting these habitats. These data open new windows onto ecosystem- and species-level behaviours, highlighting the need for wider incorporation of archaeological data in strategies for protecting our oceans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".