Conservation of Native Tree Species in The Agroforest of Rice-Based Agroecosystems Will Contribute to The Sustainable Agriculture
Bibliographic record
Abstract
Abstract Traditional agriculture relies on ecosystem services for sustainable food production and is also identified as a climate-smart approach. The present study analyses the agroforests associated with the rice farming system of three different agricultural practices for biodiversity richness by comparing two parameters: plants and birds. Out of the 9 study sites, 3 sites were traditional farms maintained by Kurichiya tribal communities, 3 were natural farms, and the other 3 farms were modern. A total of 45 families, 104 genera, 128 species of plants, and 101 bird species belonged to 48 families, and 17 orders were identified from the study sites. The sample-size-based rarefaction and extrapolation (R/E) method was adopted to identify estimated biodiversity indices. Renyi profile was used to understand the native tree diversity profile of the selected sites. The result of this study indicates that bird diversity is positively correlated with native tree diversity and NDVI of May and October. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the Kurichiya tribal community as it attracts more bird species and contributes to the biological control of pests. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".