Materials for Sustained and Controlled Release of Nutrients and Molecules To Support Plant Growth
Bibliographic record
Abstract
Controlled release fertilizers (CRFs) are a branch of materials that are designed to improve the soil release kinetics of chemical fertilizers to address problems stemming losses from runoff or other factors. Current CRFs are used but only in a limited market due to relatively high costs and doubts about their abilities to result in higher yields and increased profitability for agricultural businesses. New technologies are emerging that promise to improve the efficacy of CRFs to add additional functionality and reduce cost to make CRFs a more viable alternative to traditional chemical fertilizer treatment. CRFs that offer ways of reducing air and water pollution from fertilizer treatments, improving the ability of plants to access required nutrients, improving water retention to increase drought resistance, and reducing the amount of fertilizer needed to provide maximum crop yields are under development. A wide variety of different strategies are being considered to tackle this problem, and each approach offers different advantages and drawbacks. Agricultural industries will soon be forced to move toward more efficient and sustainable practices to respond to increasing fertilizer cost and desire for sustainable growing practices. CRFs have the potential to solve many problems in agriculture and help enable this shift while maintaining profitability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".