Century‐long stomatal density record of the nitrophyte, <i>Rubus spectabilis</i> L., from the Pacific Northwest indicates no effect of changing atmospheric carbon dioxide but a strong response to nutrient subsidy
Bibliographic record
Abstract
together form ~70% of vegetation coverage and contain the vast majority (~90%) of seabird nesting burrows. Salmonberry has in recent decades greatly expanded its coverage, while that of Tufted Hairgrass has receded. Seabirds prefer not to burrow under Salmonberry, making its ongoing expansion a potential conservation issue. We investigated three hypotheses proposed to explain Salmonberry's expansion (climate change, biopedturbation, and nutrient input), using comparisons of stomatal density of Salmonberry leaves sampled from Triangle Island, other seabird colonies, other coastal locations, and from historical specimens in herbaria. Stomatal density helps regulate photosynthetic gain and control water loss, and responds to light, nutrient, carbon dioxide, and water availability. Differing patterns of stomatal density are expected among sample locations depending on which of the hypothesized factors most strongly affects Salmonberry's performance. Our data are most consistent with the nutrient input hypothesis. We discuss possible reasons why Salmonberry has expanded so recently, even though Triangle has been a large seabird colony for at least a century and likely much longer.
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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".