Environmental drivers influencing the abundance of round sardinella (Sardinella aurita) and European sprat (Sprattus sprattus) in different areas of the Mediterranean Sea
Bibliographic record
Abstract
Data on Sardinella aurita (round sardinella) and Sprattus sprattus (European sprat) in the Mediterranean Sea are limited due to their scarce commercial interest, at least in European countries. Yet, these two small pelagic fish, sitting at opposite ends of the thermal range, could be interesting sentinel species to monitor the effects of climate change in the basin. Using the Mediterranean International Acoustic Surveys (MEDIAS) – the most extensive source of information on these species – we analyzed their biomass in several geographical subareas of the central and western Mediterranean Sea in relation to satellite-derived environmental parameters. Our findings highlight that the S. aurita biomass responded to temperature, salinity, chlorophyll concentration and sea level anomaly, depending on the GSA examined, whereas the S. sprattus biomass correlated significantly with salinity in GSA 6, with salinity and chlorophyll concentration in GSA 7, and with sea level anomaly in GSA 17. These data widen our knowledge of the factors that contribute to the ecology of these species. Further studies of their spatial distribution and of their interactions with other small pelagic species, predators and prey are needed to depict a more comprehensive scenario.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".