A Technique for Underwater Anesthesia Compared with Manual Restraint of Sea Turtles Undergoing Auditory Evoked Potential Measurements
Bibliographic record
Abstract
ABSTRACT A safe and effective technique for underwater anesthesia of green sea turtles (Chelonia mydas) was developed to allow fully submerged in-water measurements of auditory evoked potentials (AEP) without myogenic artifact. Turtles were anesthetized using medetomidine 50 μg/kg and ketamine 5 mg/kg IV and ventilated via a custom-designed double-cuffed extended endotracheal tube while submerged to an ear depth of 10 cm for up to 60 min. Procedures conducted on manually restrained turtles, submerged within range of the water surface so they could breathe voluntarily, were compared with those conducted using anesthesia, based on sea turtle venous blood gas measurements and the ability to record AEPs. Quality AEPs were recorded from both anesthetized turtles and four of seven manually restrained turtles, whereas AEP recordings were impossible for the remaining manually restrained turtles because of myogenic artifact. Manual restraint was superior to anesthesia for turtles that did not resist restraint (better venous blood oxygenation, acceptable AEPs), but anesthesia was superior to manual restraint when compared with turtles that did resist (marked lactic acidosis, and AEPs not possible). Underwater anesthesia of sea turtles is a viable option for specialized physiologic testing requiring submerged inactive subjects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".