Further support for thermal ecosystem engineering by wandering albatross
Bibliographic record
Abstract
Abstract On sub-Antarctic Marion Island, wandering albatross (Diomedea exulans) nests support high abundances of tineid moth,Pringleophaga marioni, caterpillars. Previous work proposed that the birds serve as thermal ecosystem engineers by elevating nest temperatures relative to ambient, thereby promoting growth and survival of the caterpillars. However, only 17 days of temperature data were presented previously, despite year-long nest occupation by birds. Previous sampling was also restricted to old and recently failed nests, though nests from which chicks have recently fledged are key to understanding how the engineering effect is realized. Here we build on previous work by providing nest temperature data for a full year and by sampling all three nest types. For the full duration of nest occupancy, temperatures within occupied nests are significantly higher, consistently byc. 7°C, than those in surrounding soils and abandoned nests, declining noticeably when chicks fledge. Caterpillar abundance is significantly higher in new nests compared to nests from which chicks have fledged, which in turn have higher caterpillar abundances than old nests. Combined with recent information on the life history ofP. marioni, our data suggest that caterpillars are incidentally added to the nests during nest construction, and subsequently benefit from an engineering effect.
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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 | 0.001 |
| 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) | 0.003 | 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".