Yield performance of controlled upward tapping systems in relation to rubber tree, land and labour productivity
Bibliographic record
Abstract
Three controlled upward tapping (CUT) systems which generated from the combination of downward and upward tapping with variation in length of cut, period of tapping in one year cycle, and frequency of stimulation was tested on mature phase of rubber tree. Yield performance was recorded in four consecutive tapping years. Data was analysed in view of tree, land and labour productivity. Results have showed that all three CUT systems produced rubber yield at above the target of 2.000kg/ha/year averagely. The change over tapping system of half spiral downward cut for six months to half spiral upward cut for four months (1/2S ` 6m/12, 1/2S . 4m/12) appears disadvantages on tree and labour productivity. The double cut system of half spiral downward cut for 10 months combined with a quarter spiral upward cut for 7 months (1/2S ` 10m/12 + 1/4S . 7m/12) produces highest yield on GT 1. It is likely unsuitable for PB 235 due to lower yield from the third year of tapping in comparison to the single quarter spiral upward cut. This system needs more labour input to tap rubber tree that leads to show labour productivity. The single CUT system of a quarter spiral upward cut (1/4S . 10m/12) brings a balance between tree, land and labour productivity and promising for sustainable yield in long term
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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".