Early-Arbuscular Mycorrhizal Fungi-Application Improved Physiological Performances of Sugarcane Seedling and Further Growth in the Dry Land
Bibliographic record
Abstract
The Indonesian sugarcane production in the dryland was not optimal due to physiological stress. The research to improve their physiological characteristics by arbuscular mycorrhizal application was conducted at inceptisol soil from April 2014 to February 2015, using factorial completely randomized design. The first factor was clones PS864, Kidangkencana, PS881, Bululawang, and VMC, whereas the second factor was timing of arbuscular mycorrhizal inoculum applications i.e.: in the field, nursery, and control. The results show that the mycorrhizal application in the nursery significantly increased the infection percentage (41.3%) at the age of 1 week after transplanting to the field. There are differences in physiological and growth performance of clones by the effect of timing of mycorrhizal application. Bululawang with mycorrhizal application in the nursery stage tended to have better physiological performance such as chlorophylls a, b, and total chlorophyll, photosynthesis rate, intercellular CO2 concentration, leaf area, as well as the weight of stem biomass. Mycorrhizal application in the nursery stage showed the highest increase of net assimilation rate on the PS864 and, BL clones, and the highest crop growth rate on the PS864, KK and BL clones. The mycorrizal inoculum application in the nursery increased the biomass weight of stem (11-61.1%). Infection percentage of mycorrhiza was positively correlated with the biomass weight with r2 values of 0.54.
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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 | 0.001 |
| 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".