Recent trends in micronutrient research in Saskatchewan
Bibliographic record
Abstract
In recent years, farmers have recognized the importance of micronutrient fertility for crop production.Reports of deficiency in same areas for specific crops encouraged the fertilizer industry to provide information on the role of micronutrients in plant growth.A concern for adequate available levels of copper, zinc, manganese, iron, boron and nolybdenum has stimulated demand for soil testing and plant tissue analysis services for these nutrients.Soil test reports based on criteria developed for other regions and different crops frequently recommend application of certain micronutrients to our soils.The trends indicated the need for research with Saskatchewan soils and crops to verify soil testing and plant analysis criteria and to document the potential yield benefits.The qbjectives of this study are (1) to delineate areas in Saskatchewan where response of field crops to micronutrient fertilization is most probable, {2) to evaluate soil test and plant analysis criteria and procedures, (3} to quantify possible yield responses, and {4) to identify factors affecting micronutrient availability.A variety of exploratory surveys, field trials, growth chamber experiments, and calibration tests have been enployed to meet these goals.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".