Scientific knowledge gaps on the biology of non-fish marine species across European Seas
Bibliographic record
Abstract
Available information and potential data gaps for non-fish marine organisms (cnidarians, crustaceans, echinoderms, molluscs, sponges, mammals, reptiles, and seabirds) covered by the global database SeaLifeBase were reviewed for eight marine ecosystems (Adriatic Sea, Aegean Sea, Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea, western Mediterranean Sea, Levantine Sea) across European Seas. The review of the SeaLifeBase dataset, which is based on published literature, analyzed information coverage for eight biological characteristics (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality). These characteristics are required for the development of ecosystem and ecological models to evaluate the status of marine resources and related fisheries. Our analyses revealed that information regarding these biological characteristics in the literature was far from complete across all studied areas. The level of available information was nonetheless reasonably good for sea turtles and moderate for marine mammals in some areas (Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea and western Mediterranean Sea). Further, seven of the areas have well-studied species in terms of information coverage for biological characteristics of some commercial species whereas threatened species are generally not well studied. Across areas, the most well-studied species are the cephalopod common cuttlefish (Sepia officinalis) and the crustacean Norway lobster (Nephrops norvegicus). Overall, the information gap is narrowest for length-weight relationships followed by growth and maturity, and widest for fecundity and natural mortality. Based on these insights, we provide recommendations to prioritize species with insufficient or missing biological data that are common across the studied marine ecosystems and to address data deficiencies.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".