Endomycorrhizae and Soil Ameliorant Applications on Growth Performance of Red Jabon (Anthocephalus macrophyllus) in Lime Stone Post Mining Land of PT. Holcim Indonesia Tbk
Bibliographic record
Abstract
Limestone post-mining lands often exhibit low fertility levels due to limited macro and micronutrient availability, hindering the proper growth of red Jabon plants (Anthocephalus macrophyllus).Suppressive soil is rich in microbes can be created by inoculating endomycorrhizae (arbuscular mycorrhizal fungi, AMF) and applying soil ameliorants to promote healthier growth in these conditions.This study aimed to analyze the growth performance of red Jabon in limestone post-mining lands following the application of endomycorrhizal and ameliorants.Factorial with a completely randomized design was employed, comprising three factors: endomycorrhizal treatment (M0, M1, and M2), phosphate treatment (P0 and P1), and residual cement leaching waste (CLW) treatment (L0 and L1).With 12 treatment combinations and each treatment repeated 20 times, 240 test plants were examined.Results indicated that AMF and soil ameliorant treatments effectively improved A. macrophyllus growth.Single-factor treatments of AMF and phosphate significantly impacted plant height increase and chlorophyll content in leaves while exerting no significant effect on plant diameter increase and AMF colonization.The single-factor CLW treatment did not significantly affect any measured growth parameters.Interaction effects among treatments revealed a highly significant difference in chlorophyll content in leaves but no significant differences in plant height increase, diameter increase, or AMF colonization.Additionally, AMF, phosphate, and CLW treatments influenced the formation of wood anatomical tissue proportions (xylem, phloem, cambium, and pith).A. macrophyllus plants grown in limestone post-mining lands exhibited an average root anchor index (IJA) value (0.45-1.00) in the medium category, while the root grip index (ICA) value (0.79-0.87) was classified as low.
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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".