DIVISION S-7—FOREST & RANGE SOILS Using Models to Manage Soil Inorganic Nitrogen in Forest Tree Nurseries
Bibliographic record
Abstract
ABSTRACT environmental impact of different types of N fertilizer treatments.The production of bareroot seedlings in tree nurseries requires In many aspects, the cultivation practices used tolarge amounts of inorganic fertilizers. The fertilizer type and applica-produce bareroot seedlings in forest tree nurseries aretion schedule can have an effect on seedling growth and on NO3 losses similar to those employed in agricultural fields (use ofto the environment. The objective of this study was to determine if fertilizers and pesticides, irrigation). Two important dif-a model simulating N dynamics in agricultural field soils could be used to estimate soil inorganic N levels in forest tree nursery soils. ferences, however, are the very shallow rooting depths The model selected was AGRIFLUX, a mechanistic and stochastic in forest tree nurseries (20 cm) and the recurrent fertil-model. The study was carried out from 1993 to 1995 in a Canada forest izer applications carried out during the growing season tree nursery located in Quebec. In 1994, four different treatments of of forest tree seedlings. Moreover, most forest tree nurs-N fertilization (186 kg N ha1) were applied: nine applications of eries produce bareroot seedlings in well-drained soils ammonium sulfate (AS: 21-0-0) every 2 wk compared with two and which are highly susceptible to NO3 leaching. Many mech-three seasonal applications of sulfur-coated urea (SCU: 38-0-0). Soil anistic models have been developed in recent years toinorganic N concentrations were measured at depths of 0 to 20 and simulate agricultural nutrient losses to the environment:20 to 40 cm. Temporal trends of inorganic N were generally well SOIL-SOILN (Johnsson et al., 1987), DAISY (Hansensimulated by the model for both soil depths, considering the high
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".