INFLUENCE OF COMPOST ON VEGETABLE CROP NUTRIENT MANAGEMENT
Bibliographic record
Abstract
Tomato, pepper and cucumber were grown for consecutive years using compost from two North Carolina cities (Lexington and Edenton) and McGill Composts (CMC) sources and CMC amended with Tracoderma 382. Treatments included compost with an untreated control and Telone C-35 (Telone) with and without additional fertilizer. The objective was to evaluate compost influence on yield and pest management. Results showed significant differences between treatments and among years. Cucumber and pepper had higher total and marketable yields in 2005 than in 2004. Although tomato yield was lower in 2005 than in 2004 it was evident that CMC+Telone had a higher marketable and total plant dry weight in both years. Two year data showed that combinations of treatments with CMC and Telone (Telone+fertilizer, CMC+Telone, CMC+T382) produced higher yield for tomato and cucumber. Composts from Lexington and Edenton produced more number 2 grade peppers, but treatments did not differ in total and marketable yield. In general compost treatments with or without amendments showed better results in crop yields than the control. Weed counts by species were determined on all plots. Pepper had the greatest number of weeds relative to cucumber and tomato. Organic amendments seem to increase the action of the compost source in several crops. Combination of treatments may depend on the particular crop.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".