Growth, physiology, and leachate losses in<i>Picea glauca</i>seedlings (1+0) grown in air-slit containers under different irrigation regimes
Bibliographic record
Abstract
In production tunnels, time domain reflectometry (TDR) was used to manage irrigation and leaching by controlling water content in the rhizosphere of air-slit containerized white spruce (Picea glauca (Moench) Voss) seedlings (1+0). Seedlings were exposed to four irrigation regimes (v/v: IR-60%, IR-45%, IR-30%, and IR-15%) during the first growing season to assess IR effects on growth, gas exchange, nutrient uptake, carbohydrates, root architecture, and leaching. In the province of Quebec, seedling producers generally maintain a high substrate water content (>50%, v/v) during all growth phases. The accuracy and feasibility of using TDR to decrease irrigation without affecting the material attributes of the seedlings at the end of the first growing season was confirmed. However, seedlings grown under IR-15% had significantly lower height, root collar diameter, shoot and root dry masses, root surface, root length, net photosynthesis, and nutrient contents than seedlings grown under IR-30%, IR-45%, and IR-60%. In comparison with IR-30% and IR-45%, the application of IR-60% produced no increase in shoot or root growth, mineral nutrition, and carbohydrates. Seedlings grown under IR-15%, IR-30%, and IR-45% used approximately 28, 37, and 46%, respectively, of the amount of water applied under IR-60%. Nutrient losses including anions and cations under IR-60% were higher in comparison with the other IRs. Maintaining a water content in the rhizosphere that changes with the stage of seedling development is suggested to optimize growth and to avoid excess irrigation and leaching.
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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".