A BIOPSY SYSTEM FOR SMALL CETACEANS: DARTING SUCCESS AND WOUND HEALING IN <i>TURSIOPS</i> SPP.
Bibliographic record
Abstract
Abstract Together with PAXARMS (NZ), we developed a biopsy system for small cetaceans and tested it on four populations of bottlenose dolphins (Tursiops spp.). The system consists of a modified 0.22 caliber rifle, and biopsy darts made out of polycarbonate with stainless steel biopsy tips. Animals were darted at a range of 2–15 m while travelling parallel to the vessel. Overall sampling success for obtaining biopsy samples when an animal was struck ranged from 96.6% to 100% in the four populations. However, hit rate varied for the four different populations. We did not observe a significant difference in strength of the reaction to the darting procedure when an animal was hit or missed, both among and within populations. Data from one population showed no significant difference in the reaction to biopsy sampling by four different age‐sex classes. The only factor that had a significant influence on darting success was the hit location. Furthermore, we observed a significant positive correlation between the size of the sample obtained and the reaction to biopsy sampling. Biopsy samples were sufficient for microsatellite and d‐loop analysis in 95.8% and for genetic sexing in 99% of all cases. In animals that we observed on a daily basis, wounds were healed after approximately 23 d.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".