Black spruce reforestation in Kalmia heath: seedling response to forest floor mixing and mycorrhizal inoculation with Paxillus involutus
Bibliographic record
Abstract
In the boreal forests of eastern Canada, black spruce ( Picea mariana (Mill.) Britton, Sterns, Poggenb.) regeneration often fails in nutrient-poor sites dominated by Kalmia angustifolia L. occurring after nonsevere forest fire, clearcut harvesting, and insect defoliation. Traditional silvicultural treatments have limited success in regenerating these sites. We tested whether forest floor mixing and planting black spruce seedlings preinoculated with an ectomycorrhizal (ECM) fungus, Paxillus involutus (Batsch) Fr., has potential to improve black spruce growth in Kalmia -dominated sites. We measured growth and foliar nutrient concentrations of ECM-inoculated and control black spruce seedlings planted in burned and insect-defoliated sites after forest floor mixing in Terra Nova National Park, Newfoundland. We also measured soil moisture, temperature, pH, total phenol and nutrient concentrations, litter decomposition, and soil respiration in those sites. Seedlings in soil-mixed plots had significantly higher growth and foliar nutrient concentrations than nonmixed plots, likely because soil mixing improved the substrate conditions by increased soil moisture, pH, and litter decomposition. Seedlings in insect-defoliated sites had significantly higher increases in height and foliar nutrient concentrations than those in the burned sites. Inoculated seedlings had significantly higher initial ECM formation than the control seedlings, but they failed to achieve higher growth.
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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".