Bibliographic record
Abstract
The Northeastern Region of India is incredibly rich in biodiversity, making it one of the most ecologically diverse areas in the country.This region is home to a wide variety of landforms, including forests, plains, hills, and mountains, each with its unique type of soil and climate.These diverse geographical features create an ideal environment for the growth of numerous plant, animal, and microbial species.The climate in this area further supports the flourishing of a variety of vegetation, contributing to the region's exceptional natural beauty and ecological significance.In addition to its rich natural resources, the people who inhabit the Northeastern region are known for their hardworking nature.They engage in various forms of agriculture, crafts, and other livelihoods, often working tirelessly every day to support themselves and their families.This close relationship with nature, through their work and daily practices, indirectly contributes to maintaining the rich biodiversity of the area.The activities of the people in these regions, such as farming, forest management, and sustainable practices, play a role in preserving the variety of species that call this region home.Thus, the harmony between the natural environment and the hard work of the local population sustains the region's vibrant and diverse ecosystems.
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 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 | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".