The Effect Pineaple Rind Extract (Ananas comosus L.) (Merr var. cayenne) Attack Intensity Cauliflower’s Pests (Brassica oleracea var. botrytis L. subvar. Cauliflora DC.)
Bibliographic record
Abstract
The effect of pineaple rind extract (Ananas comosus (L.) Merr var. Cayenne) towards the intensity of pests attack from cauliflower (Brassica oleracea var. botrytis L. subvar. cauliflora DC.). The purpose of this research is to find out the effect of pineaple rind extract (Ananas comosus (L.) Merr var. Cayenne) towards the intensity of pests attack from cauliflower (Brassica oleracea var. botrytis L. subvar. cauliflora DC.). This research has been held for 2 months. The field reseach was held in the field in Loa Ipuh Laut Kecamatan Tenggarong Kota. This research used Random group design (RAK) with five treatments (control included) that has been repeated for twenty five times. Every treatment was 25 %, 50 %, 75 % and the control (without treatment) then was analyzed by using Anaysis of Variance (ANOVA) and was continued with BNJ test 5 %. The result of the research showed that every value was Farithmetic (46,79) (212,3) (66,14) (194,96) (82,11) > Ftable (3,01) it can be concluded that the allotment of vegetal pesticide from pineaple rind (Ananas comosus (L.) Merr var. Cayenne) can decrease the intensity of pests attack from cauliflower (Brassica oleracea var. botrytis L. subvar. cauliflora DC.).
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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) | 0.003 | 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".