Effects of different surgical techniques: Suture material and location of incision site on the behaviour of rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
We examined the effects of midline and off midline incision locations, and incisions closed with monofilament or silk suture on spontaneous swimming behaviour in fish. Each fish had a dummy transmitter surgically implanted into its peritoneal cavity. The behaviour of fish in each treatment was estimated using three different measures: the number of C‐turns performed, the number of sprints performed, and the total distance travelled. There were no significant differences (p > 0.05) among treatments with respect to the number of C‐turns or sprints made. Fish closed with monofilament with an off midline incision travelled significantly less distance (p = 0.014) than fish closed with monofilament and a midline incision, but neither treatment was significantly different from control fish. The three measures of behaviour: C‐turns, sprints and distance travelled, were not affected either by the amount of inflammation at the incision site or by the amount of inflammation at the entry and exit points of the sutures, regardless of the treatment. The surgical techniques tested had little effect on fish behaviour post surgery, regardless of inflammation levels. It seems that behaviour of fish may not be affected by surgery, but further testing examining more subtle behaviours will have to be performed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".