Growth, camphor concentration, and nitrogen responses of white spruce (<i>Picea glauca</i>) leaves to browsing and fertilization
Bibliographic record
Abstract
We tested some responses of white spruce (Picea glauca) to fertilization, browsing, size of tree, and the season in which browsing took place. We collected leaf samples from trees that had been more or less fertilized and more or less browsed by snowshoe hares, for 9 y. We also used 3 levels of simulated browsing that removed approximately 10%, 30%, and 85% of all green biomass. We measured growth rate, camphor, and nitrogen concentrations in leaves, sampling monthly for 15 months. Camphor was induced by both natural browsing and clipping, and the response was influenced by plant resource status; camphor concentration increased with browsing pressure. At high browsing levels, both camphor concentration and growth rate declined. Seasonal camphor concentrations were highest in small trees that have a high risk of attack and during the winter, when trees are both slow growing and more heavily browsed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".