Saline Drip Irrigation and Polyethylene Mulch on Yield and Water Use Efficiency of Bell Peppers
Bibliographic record
Abstract
Saline water has been successfully applied to crops via drip irrigation. However, application of saline water through this irrigation system in combination with polyethylene mulch has not been evaluated yet. Two experiments were carried out under greenhouse conditions to evaluate effects of saline irrigation (ranging from 0.2 up to 9.0 dS·m−1 and from 0.5 to 4.5 dS·m−1, respectively) and polyethylene mulch on the yield and water use efficiency (WUE) of sweet peppers (Capsicum annuum L.). Soil temperature was higher under an infrared-transmitting polyethylene mulch than under a black mulch or bare soil. Mulched plants required less water at all salinity levels than plants grown in bare soil. Salinity levels above the control (0.2 and 0.5 dS·m−1) significantly reduced total and marketable yield and WUE. Mulched plants had greater WUE and significantly higher marketable yields than those grown in bare soil. Fruit size and pericarp thickness were significantly reduced with increasing salinity; total soluble solids (TSS) increased. Soil salinity was reduced with the use of plastic mulches relative to bare soil.
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 | 0.000 | 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) | 0.001 | 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".