Growth Curve of Elephant Foot Yam under Moderate to Severe Stress and Plant Sensitivity
Bibliographic record
Abstract
Plant response sensitivity under stress is studied to maximise yam yield for different seed weights. Longitudinal growth of 60 Elephant-foot-yam is examined in a field experiment conducted in agricultural farm at Indian Statistical Institute, Giridih, Jharkhand (India) during 2015-16. Consider the stress from harsh agro-climatic environment with little manure, no weeding, and scanty irrigation as in sustainable agriculture in starting experiment. For some plants, in the middle of experiment, as a severe stress, underground yam and root structure is detached; remaining structures of stem attached with a few roots are replanted to continue experiment. Other plants are taken off the ground and yam volumes are measured by Archimedean principle, before replanting. Regular irrigation, manuring and weeding started around the time of first intervention. Subsequently, one more interim yam reading is taken by uprooting all surviving plants with care, before replanting for maturity. Yam growth under different stress and seed weights are computed from longitudinal growth via four possible readings on yam for each surviving plant. Almost sure confidence bands are constructed to cover growth curves with certainty. Seed weight 650g corresponds to superior growth, with sharp upturn of growth curve under severe stress. Detaching yam in the middle of experiment and replanting the remaining structure to continue experiment till full plant lifetime with seed weight 650g has significantly increasing effect on yield. Yam growth is higher due to plant stress, induced from interim yam detachment. The results are new. Interim yield plus final yield exceeds normal harvest under general stress.
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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".