Relationships between microsite type and the growth and nutrition of young black spruce on post-disturbed lowland black spruce sites in eastern Canada
Bibliographic record
Abstract
The surface of the soil in recently harvested or burned lowland black spruce (Picea mariana (Mill.) BSP) sites is composed of a fine mosaic of different bryophytes (mostly Sphagnum spp. and feathermosses), disturbed organic material originating mostly from mosses at different stages of decay, and exposed mineral soil. Growth substrates were compared in lowland black spruce stands regenerating after either careful logging or wildfire. The 3-year annual increment for black spruce seedlings was greatest with substrates of feathermosses, mainly Pleurozium schreberi (Brid.) Mitt., fibric material of P. schreberi origin, and a mixture of fibric P. schreberi and humic materials; it was least with fibric Sphagnum spp., mineral soil, and decaying wood substrates. The most favourable substrates for growth were characterized by better black spruce N and P foliar status. Our results also suggest that categories of growth substrates in the rooting zone reflect nutritional quality better than categories of growth substrates on the soil surface. To maintain or increase black spruce growth following careful logging of sites prone to paludification, we recommend fill-planting of seedlings in substrates originating from P. schreberi; management techniques that favour P. schreberi over Sphagnum mosses should also be developed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".