Replication data for: Heat tolerance decreases and cold tolerance increases with elevation for a species-rich insect family on a tropical volcano
Bibliographic record
Abstract
These are the data, phylogenetic trees, and code for our analysis of the change in thermal tolerance with elevation for the Staphylinidae community on Volcan Cacao, in Área de Conservación Guanacaste, Costa Rica. We set out to determine how heat and cold tolerance, as well as thermal tolerance breadth (via a statistical proxy) changed with elevation for this beetle community, and to contextualize the heat tolerance values against environmental temperatures and against other insect heat tolerance data from one of the most popular thermal tolerance databases in the literature. We found that heat tolerance and thermal breadth decreased with elevation while cold tolerances slightly increased, all of which were according to theory. These results (particularly heat tolerances), however, went against the patterns previously described for ectotherms using this popular thermal tolerance database. This is a replication dataset for the second chapter of my PhD thesis. The chapter has been accepted for publication, so this dataset will be associated as a supplementary material for the paper. Here you will find thermal tolerance data for Costa Rican beetles, and all associated supporting data files, code, and prose that make up the manuscript.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.072 |
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".