Mechanisms of interaction between <i>Kalmia angustifolia</i> cover and <i>Picea mariana</i> seedlings
Bibliographic record
Abstract
Sites dominated by Kalmia angustifolia L. are often associated with slow decomposition of organic matter, decreased nitrogen (N) mineralization rates, and low black spruce (Picea mariana (Mill.) BSP) productivity. The objective of this study was to separate the effects of belowground competition by Kalmia from the effects of water-soluble soil phenols on black spruce seedlings growing under different levels of Kalmia cover. A factorial greenhouse bioassay was established in which black spruce seedlings were grown for 6 months in intact blocks of soil with three different levels of Kalmia cover. The soil was treated with charcoal to reduce the amounts of water-soluble phenols, and (or) tubes were inserted to exclude Kalmia roots. At low Kalmia cover, reducing the level of belowground competition increased seedling biomass by 134%. However, reducing belowground competition did not increase seedling biomass at the two higher levels of Kalmia cover. It is possible that seedling biomass remained low because of an increased immobilization of N in the organic layer. Furthermore, the proportion of ectomycorrhiza morphotypes differed among seedlings growing under different levels of Kalmia cover. The effect of water-soluble phenols on seedling growth remains uncertain, since we observed a confounding effect of the charcoal treatment on soil microbial biomass and seedling response.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".