How to shift unproductive <i>Kalmia angustifolia – Rhododendron</i> <i>groenlandicum</i> heath to productive conifer plantation
Bibliographic record
Abstract
Conifer-regeneration failure is often observed on sites invaded by ericaceous shrubs. In northeastern Quebec, Canada, these sites are frequently characterized by dense Kalmia angustifolia L. – Rhododendron groenlandicum (Oeder) K.A. Kron & Judd cover. Such failures are potential consequences of nutrient limitation, allelopathy, or low soil temperatures. Conversion of productive forests into heaths poses a threat to the maintenance of forest productivity and biodiversity. We evaluated scarification, spot fertilization, and increased seedling foliar N concentration as treatments to promote planted black spruce (Picea mariana (Mill.) BSP) seedling survival and growth. We measured seedling, vegetation, and soil responses to the treatments for 5 years following planting. Scarification had positive impacts on seedling growth: the differences between scarified and unscarified plots increased over time, and double-pass scarification proved slightly more effective than a single-pass treatment. Responses to scarification were enhanced when seedlings were fertilized. A slow-release fertilizer with micronutrients proved slightly more effective than the 26N–12P–6K formulation; the latter also induced higher mortality than the former or no fertilizer. Gains due to increased N concentrations based on nursery practices were significant but short-lived. Our results demonstrate how silviculture and nursery practices can be used for resetting the secondary succession where ecosystem retrogression is observed following K. angustifolia – R. groenlandicum invasion.
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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) | 0.002 | 0.001 |
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".