Low-temperature tolerances of tropical fish with potential transgenic applications in relation to winter water temperatures in Canada
Bibliographic record
Abstract
Application of fluorescent protein transgenes in tropical freshwater fish is used in research (e.g., in zebrafish) and in the commercial ornamental aquarium trade. To assess the overwinter potential of such fish in Canada, we examined the minimum temperature tolerance of three wild-type species (zebrafish, Danio rerio (Hamilton, 1822); black tetra, Gymnocorymbus ternetzi (Boulenger, 1895); tiger barb, Puntius tetrazona (Bleeker, 1855)) used as fluorescent models in the aquarium trade, as well as four lines of green fluorescent protein (GFP) transgenic and wild-type zebrafish used in research. Both tetra and barb had lower temperature tolerance limits that were higher than surveyed winter temperatures in Canada, and the lower tolerance limits of zebrafish lines was only within the range of a few surveyed lakes in some years. One line of GFP zebrafish had a lower temperature tolerance limit that was significantly higher than those of other lines, suggesting that some fluorescent transgene constructs may alter tolerance to extreme temperatures. When lines of zebrafish were reared at 8, 10, or 28 °C for 4 months, all lines were capable of producing viable embryos after cold rearing. These results indicate that tropical transgenic fish models used in research and in the aquarium trade are not expected to persist over winter in Canadian water systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".