Heated competition: how climate change will affect non‐native pumpkinseed <i>Lepomis gibbosus</i> and native perch <i>Perca fluviatilis</i> interactions in the U.K.
Bibliographic record
Abstract
Heated and ambient temperature experimental ponds were used to examine competition between introduced pumpkinseed Lepomis gibbosus and native perch Perca fluviatilis in England, U.K., and how these interactions are likely to change under climate warming conditions. Results from three sets of two-week experiments indicated that in both species and in all sets, growth was faster in heated than in ambient temperature ponds. Growth of both P. fluviatilis and L. gibbosus in sympatry did not differ significantly from that observed in allopatric ponds. Diet analysis indicated that increased resource partitioning occurred when P. fluviatilis and L. gibbosus were reared in sympatry, with P. fluviatilis shifting to a diet higher in microcrustaceans. The results do not support the previous claims of adverse effects of L. gibbosus on P. fluviatilis populations. Under conditions of climate change, however, which have been demonstrated experimentally to enhance L. gibbosus recruitment, this species is expected to become invasive in England, resulting in higher densities that may exert a stronger competitive effect than examined in this study.
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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 | 0.000 |
| 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".