CHARACTERISING INDICATORS OF SUSTAINABLE LAND MANAGEMENT IN INDIAN HIMALAYAN SLOPING LANDS
Bibliographic record
Abstract
Viewing environmental perspectives and growing concerns related to ecological balance in nature together with social, agricultural, industrial and economic developments, sustainable development of ecosystems has become a crucial issue with a particularity to hill and mountain regions around the world. Out of a vast coverage on sustainable development, SLM (Sustainable Land Management) is one important ecosystem module that itself has a wider expansion and is construed of several folds and dimensions which have been standardised well by an international working group consisted of Agriculture Canada, IBSRAM (now merged with IWMI), FAO, TROPSOIL, USDA-SCS, IFDC and others in the form of a standardised guideline, i.e., FESLM (Framework for Evaluating Sustainable Land Management). In view of a comprehensive account on SLM, indicators of sustainability of land management have been characterised on the basis of five pillars viz. productivity, security, protection, economic viability and acceptability in the hill areas covering a long stretch of western, eastern and entire north-eastern Himalayas encompassing the states of J&K, Himachal Pradesh, Uttaranchal, Assam Sikkim, Arunachal Pradesh, Meghalaya, Manipur etc. in the present study. Various parameters have been chosen to carve out indicators satisfying each basic five pillar of the FESLM standard. Also, the efficacy of these indicators has been observed on some of the important agricultural systems being used in practice in different sloping lands in India; thus, it has been concluded that the sustainability needs to be enhanced in north-eastern Himalayan farming systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".