Fine-scale habitat selection by coastal bottlenose dolphins: application of a new land-based video-montage technique
Bibliographic record
Abstract
Cetacean distribution and underwater topography are frequently correlated. These patterns are commonly studied on large spatial scales, over tens of kilometres, but very rarely on a fine scale. Sightings of bottlenose dolphins, Tursiops truncatus, within the Moray Firth, Scotland, were previously found to be concentrated within deep, narrow channels. To understand why such areas were selected, more-detailed information on the distribution of dolphins was required. This study describes the development of a video technique to study the spatial distribution and relative abundance of bottlenose dolphins. We then used the methodology to investigate whether water depth and seabed gradient influence the dolphins' distribution patterns. Furthermore, temporal patterns of use were examined with respect to seasonal, tidal, and diurnal cycles. The distribution of dolphins was significantly related to topography: dolphins were sighted most frequently in the deepest regions with the steepest seabed gradients. There was a clear temporal pattern in the use of the area, with sightings peaking during July. However, the presence of dolphins was not significantly related to tidal or diurnal cycles. The topography of the area appears to be a significant influence on its intensive use by dolphins, and patterns of use indicate that topography may facilitate foraging during seasonal migrations of fish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".