Spatial Variability of Substrate Water Content and Growth of White Spruce Seedlings
Bibliographic record
Abstract
Irrigation by jet‐type sprinklers contributes to the spatial variability of substrate water content and growth of containerized white spruce [ Picea glauca (Moench) Voss.] seedlings grown outdoors during their second growing season. Geostatistical analyses were used to identify the spatial structure of this variability throughout the growing season and to help develop a sampling strategy to facilitate irrigation management. Boundary line analysis confirmed that the heterogeneity of height growth is related to seasonal variations in substrate water content and that maximum height growth and seedling biomass is attained when average seasonal substrate water content is approximately 40% (v/v). Parameters estimated from semi‐variograms, most notably the range (a) and total variance ( C 0 + C 1 ) of substrate water content, can be used to define sampling strategies specific to irrigation management and morphophysiological evaluation of seedlings. The relationship between leaching and substrate water content can be used, in conjunction with kriged maps, to estimate potential losses of mineral nutrients and to quantify water use for the production of white spruce seedlings during their second growing season in a forest nursery. More than 15% of the seedlings in the crop used in the present study were rejected at delivery. Knowledge of the spatial variability within a crop enables forest nurserymen to modify sampling techniques and cultural practices, produce more uniform seedlings and reduce the quantity of seedlings that fail to meet morphophysiological criteria.
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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) | 0.000 | 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".