Improving French Bean (PhaseolusVulgaris L.) Pod Yield and Quality Through the Use of Different Coloured Agronet Covers
Bibliographic record
Abstract
French bean (Phaseolus vulgaris L.) is among important vegetables in supplying proteins, vitamins, minerals and dietary fiber to humans worldwide. Its successful production in the tropics is, however, constrained by abiotic and biotic stresses as the crop is predominantly grown in open fields. Netting technology has been proved successful in protecting crops against adverse weather and insect pests. Coloured net technology is an emerging technology, which introduces additional benefits on top of the various protective functions of nettings. Two trials were conducted at the Horticulture Research and Teaching Field, Egerton University, Kenya to evaluate the effects of different coloured agronet covers on growth, pod yield and quality of French bean. A randomized complete block design (RCBD) with six treatments and four replications was used. French bean plants were grown under a white, blue, yellow, tricolour or grey net cover with open field production as the control. Variables measured included days to emergence and emergence percentage (%), stem collar diameter, plant height, number of branches and internodes, internode length and crop yield. French bean grown under the different coloured net covers showed relatively better growth and crop performance marked by more pods and higher total yields and percentage of marketable yields compared to those grown in the open field. Growing French bean under net covers hastened the rate of pod maturation more-so under the light-coloured colour-nets. Findings of this study demonstrate the potential of coloured net covers in improving French bean pod yield and quality under tropical field conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".