Bibliographic record
Abstract
Natural change in climate is slow and takes millions of years; and it is known to have made our planet hospitable to live. The climate change is not limited to one country or a continent. It is occurring across the globe as evident from droughts in Texas and flooding along the Missouri River in the United States and along the Red River in Canada. Climate change drives many stressors and interacts with many non-climatic stressors which make it difficult to forecast outcomes in any general way other than existing threats to agriculture. Agroforestry increases a high level of diversity within agricultural lands which supports numerous ecological and production services that bring resilience to the impact of climate change mitigation and adaptation. Climate change risk management is difficult in annual cropping systems due to increasing uncertainty of inter-annual variability in rainfall and temperature. Mixing of woody trees with crops, forage and livestock operations provides greater resilience to the inter-annual variability through crop diversification and increased resource use efficiency. Deep rooted trees allow better access to nutrients and water during droughts and when appropriately integrated into annual cropping systems and extract from different resource pools that would otherwise be lost from systems. Agroforestry increases soil porosity, reduces runoff and increases soil cover, which improve water infiltration and reduces moisture stress in low rainfall years. During periods of excessive soil moisture, tree based systems keep soils aerated by pumping out excess water and offer an economic return. The book contains 36 chapters mainly on agroforestry practices found in India and its role in climate change mitigation and adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".