Drip irrigation with film covering improves soil enzymes and muskmelon growth in the greenhouse
Bibliographic record
Abstract
The present study evaluated responses of soil enzyme activity, soil micro-organisms, muskmelon root growth and muskmelon fruit yield and quality to different levels of film covering (full, half and no plastic film covering), drip pipe density (one pipe for one row (T1), three pipes for four rows (T3/4) and one pipe for two rows (T1/2)) and different lower limits of irrigation (60%, 70% and 80% of field capacity) in a greenhouse experiment using an orthogonal experimental design. Half film mulch cover resulted in higher muskmelon root activity (second only to full film covering) during the fruit swelling growth stage and promoted soil micro-organism growth. Compared with full and no film cover conditions, under half film cover mean soil urease activity was 25.16% and 1.46% higher, alkaline phosphatase (ALP) activity was 18.42% and 16.89% higher and catalase activity was 24.20% and 17.24% higher respectively. Compared with T1 and T3/4, under T1/2 mean soil ALP activity was 22.36% and 5.76% higher, catalase activity was 2.45% and 10.57% higher and muskmelon root activity during the fruit swelling period was 1.13- and 3.98-fold higher respectively. Irrigation at both 60% and 80% of field capacity improved muskmelon root length and area, soil micro-organism populations and soil urease, ALP and catalase activity. In addition, half film covering, irrigation at 80% field capacity and T1/2 improved the partial factor productivity for nitrogen, yield and fruit quality of muskmelons in the greenhouse. Therefore, these conditions appear to be the most appropriate agronomic configuration for muskmelon cultivation in greenhouses.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".