MEDLEM database, a data collection on large Elasmobranchs in the Mediterranean and Black seas
Bibliographic record
Abstract
The Mediterranean Large Elasmobranchs Monitoring (MEDLEM) database contains over 3000 records (more than 4000 individuals) of large elasmobranch species from 20 different countries around the Mediterranean and Black seas, observed from 1666 to 2017. The main species included in the archive are the devil fish (1 813 individuals), the basking shark (939 individuals), the blue shark (585 individuals) and the great white shark (337 individuals).In the last decades other species such as the shortfin mako (166 individuals), the spiny butterfly ray (138) and the thresher shark (174 individuals) were reported with an increasing frequency. This was possibly due to an increased public awareness on the conservation status of sharks, and a consequent development of new monitoring programmes. MEDLEM does not have a homogeneous reporting coverage throughout the Mediterranean and Black seas and it should be considered as a database of observed species presence. Scientific monitoring efforts in the south-eastern Mediterranean and Black seas are generally lower than in the northern sectors and the absence in our database of some species does not imply their actual absence in these regions. Some considerations are made on the frequency and spatial distribution of records, size structure of the observed individuals for selected species, general area coverage and species involved as by-catch by fishing gear.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".