Effets de différentes régies d'irrigation sur la croissance, la nutrition minérale et le lessivage des éléments nutritifs des semis d'épinette noire (1+0) produits en récipients à parois ajourées en pépinière forestière
Bibliographic record
Abstract
To reduce the quantity of irrigation water used and the amount of mineral nutrients lost because of leaching, we used time domain reflectometry to monitor and maintain four irrigation regimes (15, 30, 45 and 60%, v/v) during the first growing season for 1+0 black spruce (Picea mariana (Mill.) BSP) seedlings. The seedlings were produced in air-slit containers (IPL 25350A), filled with a peat substrate and were grown under a polyethylene tunnel at a forest nursery. Similar fertility levels were maintained in all four irrigation regimes even though the water content of the substrate could be very low (15 and 30%). Irrigation regime did not affect growth, root architecture or tissue nutrient contents at the end of the growing season. Monitoring water use over the course of the growing season clearly showed that the amount of irrigation water could be reduced by 62 to 76% without compromising seedling quality relative to the 60% irrigation regime. Leachate losses varied exponentially as a function of irrigation regime. The mean amount of water leached, relative to the quantity of water applied during the sampling period, was 10, 7.1, 28.4, and 62.2% for the 15, 30, 45, and 60% irrigation regimes, respectively. The losses of mineral nitrogen at the beginning of August were 49.7, 35.9, 55.2, and 88.2%, respectively, for the 15, 30, 45, and 60% irrigation regimes. To optimize irrigation and decrease leaching, a dynamic model for irrigation management is proposed that accounts for the phenological development of black spruce seedlings grown under tunnel conditions in forest nurseries.
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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 | 0.001 |
| 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".