Great Plains cropping system studies for soil quality assessment
Bibliographic record
Abstract
Abstract Interactions between environmental conditions and management practices can significantly affect soil function. Soil quality assessments may improve our understanding of how soils interact with the hydrosphere and atmosphere. This information can then be used to develop management practices that improve the capacity of the soil to perform its various functions and help identify physical, chemical, and biological soil attributes to quantify the present state of a soil and detect changes resulting from management. In protocols established by the Great Plains cropping system network, sampling and testing procedures were selected to identify physical, chemical, and biological soil attributes responsive to management that may serve as useful indicators in assessing the effects of management on the soil resource. Eight existing long-term studies from throughout the Great Plains in the central USA were used to make these assessments because, (1) many years are required for certain soil properties to change measurably; (2) annual weather causes variation in system performance; and (3) the soil pools of interest are spatially variable. This paper includes detailed descriptions of the treatments and sites, and both long-term and short-term (1999–2002) data on precipitation, temperature, and yields for each location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".