Population dynamics response of <i>Lupinus arcticus </i>to fertilization, clipping, and neighbour removal in the understory of the boreal forest
Bibliographic record
Abstract
A demographic study was conducted on field populations of Lupinus arcticus S. Wats. growing in the understory of a white spruce dominated forest, near Kluane Lake, Yukon. The relative effects of soil fertility level, neighbours, and herbivory were assessed using a factorial experiment of ± fertilizer (N-P-K), ± neighbour removal, and ± clipping. We monitored the dynamics of leaves and collected data on reproduction, survival, and size for two growing seasons. Fertilizing increased the incidence of disease on leaves and reduced reproductive efficiency. Clipping reduced leaf cohort survivorship, total leaf density, and the incidence of disease on leaves. Removing neighbours increased the percent cover of L. arcticus and decreased total leaf mortality. Treatments had no effect on the survival of leaves in early cohorts. Although there were some significant responses to treatments, the overall tendency was a lack of response, especially pertaining to leaf population dynamics. This low response to the treatments imposed is consistent with the argument that plants growing in low productivity, infrequently disturbed habitats should show little response to short-term changes in local environmental conditions. The results are also consistent with suggestions that plants in moderately stressed habitats should be more adapted to withstand grazing than competition.Key words: boreal forest, competition, demography, fertilization, population dynamics, Lupinus arcticus.
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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) | 0.000 | 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 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".