HAMA TANAMAN BELIMBING DI WILAYAH KABUPATEN BLITAR JAWA TIMUR
Bibliographic record
Abstract
Pests of star fruit in Blitar District, East Java. Pest attack causes the production of star fruit decreases. This study aim was to identify the pests of star fruit in Blitar District which could provide an information about pest species, damage symptoms and damage intensity. The research was conducted during August – September 2014 in Gogodeso, Karangsonoand Pojok Villages at Blitar District, East Java. Observations of the pests ware done by determining the four locations of plantation on each villages randomly. Three plants sample were choosen in each plantation location diagonally. Each plant was divided into four quadrant according to wind directions. One branch in the bottom was choosen in each quadrant. Nine species were recorded as pests of starfruit in Blitar district. Pest was identified on the leaf was Pteroma plagiophleps, on the branch was Zeuzera coffeae, on the flower were Toxoptera aurantii and Maconellicoccus hirsutus, whereas those on the fruit were Toxoptera aurantii, Maconellicoccus hirsutus, Thrips javanicus, Helopeltis bradyi, Cryptophlebia leucotreta, Bactrocera dorsalis, and Bactrocera carambolae. The population density of M. hirsutus was higher in Karangsono than in other villages. The highest damage intensity was caused by Bactrocera spp., C. leucotreta and H. bradyi (occured in Gogodeso), whereas the highest damage intensity of T. javanicus occurred in Pojok.
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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 | 0.001 | 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) | 0.007 | 0.001 |
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".