High value crops in coarse-textured soil and nitrate leaching - How risky is it?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To identify practices that minimize the risk of NO 3 -N leaching to groundwater from high-value crops produced on coarse-textured soil, NO 3 -N movement was determined from varied water (overhead vs. drip irrigation) and nutrient (pre-plant broadcast vs. fertigation) management in cucumber (Cucumis sativus L.) over three growing seasons, and municipal compost rate and mulch type in ginseng (Panax quinquefolius L.) over 5 yr. Under cucumber, seasonal NO 3 -N leaching ranged from 4 to 28 kg ha -1 , and was reduced by 10 kg NO 3 -N ha -1 yr -1 in 2 of 3 yr using drip delivery of water and nutrients as compared with pre-plant broadcast fertilizer with overhead irrigation. Under ginseng, 452, 321 and 173 kg NO 3 -N ha -1 leached from C 260 compost (170 Mg ha -1 incorporated compost under 90 Mg ha -1 compost mulch re-applied annually), C 200 bark (200 Mg ha -1 incorporated compost under pine bark mulch) and C0 straw (no compost, straw mulch), respectively, during the initial fall plus subsequent 4 yr. Following fall application of compost, subsoil solution NO 3 -N concentrations increased to > 100 mg NO 3 -N L -1 by early December. Even with no compost applied, NO 3 -N concentrations in water draining from the ginseng soil profile usually exceeded the drinking water standard. Key words: Compost, cucumber, fertigation, ginseng, nutrient management
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it