How Will Climate Warming Affect Non-Native Pumpkinseed Lepomis gibbosus Populations in the U.K.?
Bibliographic record
Abstract
Of the non-native fishes introduced to the U.K., the pumpkinseed is one of six species predicted to benefit from the forecasted climate warming conditions. To demonstrate the potential response of adults and their progeny to a water temperature increase, investigations of parental pumpkinseed acclimatization, reproduction and YOY over-wintering were carried out in outdoor experimental ponds under ambient and elevated water temperature regimes. No temperature effects were observed on either adult survivorship and growth, and none of the assessed reproductive activity variables (total spawning time, spawning season length, number of spawning bouts) appeared to be responsible for the large differences observed in progeny number and biomass. However, it was demonstrated in a previous study [Zięba G. et al., 2010] that adults in the heated ponds began spawning earlier than those of the ambient ponds. Ambient ponds produced 2.8× more progeny than the heated ponds, but these progeny were significantly smaller, probably due to their late hatching date, and subsequently suffered very high mortality over the first winter. Pumpkinseed in the U.K. will clearly benefit from climate warming through earlier seasonal reproduction, resulting in larger progeny going into winter, and as a result, higher over-winter survivorship would be expected relative to that which occurs under the present climatic regime.
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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 | 0.000 |
| 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".