Quantifying sublethal Florida manatee-watercraft interactions by examining scars on manatee carcasses
Bibliographic record
Abstract
A leading human-related threat to the Florida manateeTrichechus manatus latirostrisis collisions with watercraft, which account for 20-25% of reported mortalities. Quantitative threat assessments do not include information on all known manatee-watercraft interactions. These interactions often result in sublethal wounding, usually leaving multiple fresh external wounds in a variety of patterns. These wounds then resolve into well-healed scars. We characterized and quantified watercraft-related scar patterns (1 pattern = 1 strike event) on 2935 nonperinatal carcasses (>150 cm total length) that were recovered from 2007 through 2016 to compare the number of patterns by life stage, sex, and population region and across years. We used generalized linear mixed models to examine the effects of several factors on the probability carcasses having scars and on the number of scar patterns per carcass. The models indicated that approximately 96% of adults, approximately 70% of subadults, and approximately 34% of calves had watercraft-related scars. The raw data showed that 1 in 4 adults had been hit 10 or more times; 5 adult carcasses bore evidence of 40 or more strikes. On average, adult females had more scar patterns than did adult males. Manatees on Florida’s west coast had more scar patterns than did those on the east coast, while carcasses from the less populated Everglades had significantly fewer scar patterns than did those from the rest of the state. These results improve our understanding of the extent of sublethal injury of the Florida manatee caused by boat strikes.
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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 | 0.000 | 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 | 0.001 |
| 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".