Fish farming and its appeal to common bottlenose dolphins: modelling habitat use in a Mediterranean embayment
Bibliographic record
Abstract
ABSTRACT 1. Common bottlenose dolphins Tursiops truncatus interact with fish farms in the Mediterranean Sea. These interactions were investigated in a Greek bay by incorporating multiple geographic, bathymetric, oceanographic, and anthropogenic variables. 2. Generalized additive models (GAMs) and generalized estimation equations (GEEs) were used to describe dolphin presence. Visual surveys were conducted over 2909 km under favourable viewing conditions that included 54 dolphin group follows for 457 km. Sea surface temperature (SST) and chlorophyll‐a (Chl‐a) data were obtained from remote sensing imagery, and distances to sources of human influences including fish farms, a ferro‐nickel plant, and a slag disposal area were calculated within a geographic information system (GIS). 3. Bottlenose dolphins were encountered mainly in the south‐eastern portion of the study area, and occurrence was not clearly related to SST and Chl‐a, nor the ferro‐nickel plant or nearby slag disposal area. 4. Dolphin occurrence generally increased within 20 km of fish farms, with four farms and dolphins displaying a positive relationship, seven no clear relationship, and two a negative one. 5. While it is likely that uneaten food and other detritus attract dolphin prey, individual farms (or clusters of farms) clearly had a different appeal. The proximity of the ferro‐nickel plant and slag disposal area to ‘attractive’ fish farms could compromise dolphin health, but physiological data are unavailable. 6. The modelling of multiple variables allowed for a description of dolphin habitat use and attraction to some fish farms. More such data analysed in similar manner would be instructive for other areas where marine mammals and fish farms co‐occur. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".