Granular Calcite Stimulates Natural Mycorrhization and Growth of White Spruce Seedlings in Peat-Based Substrates in Forest Nursery
Bibliographic record
Abstract
The acidity of peat-based substrates used in forest nurseries limits seedling mineral nutrition and growth as well as the activity of microorganisms. To our knowledge, no study has yet evaluated the use of granular calcite as a covering material to increase pH, calcium and CO2 concentrations in the rhizosphere and ectomycorrhizal development. The objective is to compare different covering treatments on early colonization of the roots by ectomycorrhizal fungi, as well as the growth and calcium nutrition of white spruce seedlings in the forest nursery. Three treatments were used to cover the plant cavities (Silica (29 g/cavity; control treatment), Calcite (24 g/cavity) and calcite+ (31 g/cavity)) and were distributed randomly inside each of the five complete blocks of the experimental design. The results show that calcite stimulates natural mycorrhization. Seedlings grown with calcite have significant gains for several growth and physiological variables, and that the periphery of their root plugs are more colonized by the extramatrical phase of ectomycorrhizal fungi, thus improving root-plug cohesion. The authors discuss the operational scope of the results in relation to the tolerance of seedlings to environmental stress and the improvement of their quality, both in the nursery and in reforestation sites.
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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) | 0.001 | 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".