Photo‐identification of short‐beaked common dolphins <i>(Delphinus delphis)</i> in north‐east New Zealand: A photo‐catalogue of recognisable individuals
Bibliographic record
Abstract
Abstract Photo‐identification has been established as a helpful tool in cetacean research. However, no study to date has attempted to apply this method to short‐beaked common dolphins (Delphinus delphis L.). We present here the results of two studies that were conducted concurrently in Mercury Bay and the Hauraki Gulf on the north‐east coast of New Zealand's North Island. Methods for distinguishing between individual dolphins are discussed. Sighting records of recognisable individuals indicate that some common dolphins move between Mercury Bay and the Hauraki Gulf (100 km distance), as well as between Mercury Bay and Whakatane (200 km distance). Common dolphin abundance and site fidelity appeared to be greater in the Hauraki Gulf than in Mercury Bay. A selection of photographs of distinct individuals is presented to allow future studies to compare their sighting records to ours, which may help establish the extent of home ranges, site fidelity, and possibly even longevity for common dolphins.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".