Does Knitted Shade Provide Temperature Reduction and Increase Yield Kale?
Bibliographic record
Abstract
We aimed to evaluate whether the air temperature, soil temperature, and luminosity in a low tunnel covered with agricultural mesh screening affected the characteristics of kale production. The study was conducted on the cultivation of kale in six different growing environments. The experimental setup consisted of randomized block design (RBD) with factorial analysis (2 × 6) with four repetitions. The kale (Brassica oleracea L. var. acephala) hybrids Hi Crop and Kobe F1 were used as plant material. The growing environments were open field and protected environments consisting of low tunnels, each covered with a different mesh screen: red, thermo-reflective silver, black, tissue-non-tissue (TNT), and organza fabric. Sensors were installed within each environment to monitor air temperature and soil temperature. The TNT screen resulted in the highest air and soil temperatures and lower yield. The black mesh resulted in lower temperatures than other coverings. Organza fabric provided the best yield (22.8%) compared to open field and it was 9.89to42.19 % more productive compared to the other meshes. Organza fabric was the best environment for the cultivation of kale in tropical climates. These data confirm that kale biomass production was greatly affected by stress high temperature.
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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) | 0.001 | 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".