Relationships among Mineralizable Soil Nitrogen, Soil Properties, and Climatic Indices
Bibliographic record
Abstract
Soil N mineralization is an important N contributor to crop uptake; however, the soil and climatic controls on soil mineralizable N are poorly understood. Soil samples from 56 sites across Canada were used to determine the potential to predict the size of mineralizable N pools through simple soil properties and through simple climatic indices and the re_clim indices. Mineralizable N was determined using a 24‐wk aerobic incubation at 25°C. Potentially mineralizable N (N 0 ) was estimated by curve fitting using N mineralized from 2 to 24 wk, and Pool I, a labile mineralizable N pool, was determined as the N mineralized in the first 2‐wk period. Soil properties were relatively effective predictors of N 0 with soil organic N (SON) and sand explaining 40 and 34% of the variability, respectively. Particulate organic matter N (POM‐N) and pH explained 18 and 25%, respectively, of the variability in Pool I. Simple climate normals were generally poor predictors of pool size except for potential evapotranspiration (PET), which predicted 24% of the variability in Pool I. The re_clim indices, normally applied to the activity of soil decomposers and applied here for the first time to explain soil mineralizable N pool size variability, performed better than simple climate indices and explained up to 26% of the variation in N 0 By including soil and climatic parameters in a multiple regression model, it was possible to explain about 63 and 40% of the variability in N 0 and Pool I, respectively, across a wide range of arable soils in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".