Comparative Evaluation of Four Presumptive Tests for Blood to Detect Epithelial Injury on Fish
Bibliographic record
Abstract
Abstract Current methods of fish epithelial injury detection are limited to gross macroscopic examination that has a subjective bias as well as an inability to reliably quantify the degree of injury. Fluorescein, a presumptive test for blood, has been shown to have the capability to detect and quantify fish epithelial injury. However, there are several other presumptive tests for blood (Bluestar©, phenolphthalein, and Hemastix®) that may have benefits over the use of fluorescein, particularly for field research on wild fish. This study investigated the capabilities of these four tests to detect and quantify a variety of injuries commonly encountered by fish (abrasion, cuts, fin frays, and punctures) using the freshwater bluegill Lepomis macrochirus as a model. Fluorescein was consistently found to be the most reliable (i.e., detected the highest proportion of true positive results and rarely detected false positive reactions) of the four presumptive tests for blood compared. Further testing was conducted to examine the reliability of fluorescein. By 24 h after an injury was inflicted, the injury was no longer detectable by fluorescein, and when fluorescein was applied to an injured fish, the fluorescein was no longer detectable 3 h after application. In a comparison of two common anaesthetics used in fisheries research, there was no significant difference in the proportion of injury detected when 3-aminobenzoic acid ethyl ester methanesulfate (tricaine) was used compared with a clove oil and ethanol (1∶9) solution. In summary, fluorescein was the most reliable presumptive test for blood examined in this study for the detection and quantification of recent (hours) fish epithelial injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".