Beräkning av olika odlingsåtgärders inverkan på kväveutlakningen
Bibliographic record
Abstract
An empirical method for estimation of nitrogen (N) leaching on field and farm level was presented. The work was financed by, and in corporation with, the Swedish Board of Agriculture. The method is a further development of a module in the data programme STANK (SJV, 2001) which is used for calculation of nutrient flows on farms. The main objective was to visualize important factors which contribute to the risk for N leaching, with focus on those which are easily influenced by measures taken in the field. To describe the general risk for N leaching, in a certain area on a specific soil, data were used from a project where N leaching in Sweden was estimated for different soils and crops (Johnsson & Mårtensson, 2002). Back‐ground leaching for different regions (289 communities) and soils (5 classes based on clay content) was calculated. The back‐ground leaching represented N leaching from a cereal crop with nitrogen applied as commercial fertilizer in appropriate amounts and with soil tillage in September to October. To estimate the total risk for N leaching under different conditions the following parameters were considered most important: Time for tillage Dose of fertilizer N in relation to recommended dose Time and technique for spreading of manure N fertilization during autumn N uptake in crops during autumn Residual effects of crops To calculate the effect of time for tillage, a factor (based on 5 soil texture classes and 6 classes for tillage) was multiplied with the back‐ground leaching. The other parameters were additive and based on an attempt to estimate the amounts of mineral N in the soil in comparison with the conditions in the "back‐ground" situation. Of the mineral N in the soil a certain fraction (0.1‐0.3, depending on clay content and climatic conditions) was assumed to contribute to N leaching. N uptake in crops during autumn was varied depending on the amount of mineral N present in the soil during autumn (including fertilization). The model was mainly based on an empirical approach but partly also process based. Results from field leaching experiments in Sweden, data in literature, experiences and numerous assumptions were used to make an overview of the different factors contributing to N leaching. With this model it is possible to estimate the risk for N leaching and to extract the effect of different measures taken in the field. The model showed good agreement with measurements in field experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".