Study Of Flora And Soil Quality Of Selected Chromite Mining OB Dumps In Sukinda, Odisha, India.
Bibliographic record
Abstract
The nature plays a very significant role in the maintenance of ecological order of the ecosystem. The forests, occupy unique position because of its renewable nature. Forests also preside protection to other resources and crops. The species diversity in a plant community increases with the decrease in anthropogenic disturbances. As a result of mining and coal combustion significant areas of land are degraded and acting ecosystems are replayed by undesirable waste materials in the form of dumps, tailing dams and ash dams. The dumping of mine tailings and other rejected materials (referred to as overburden, OB) generated from opencast metal mines is considered as a major contributor to the ecological and environnemental degradation. Plant communities are often subjected to disturbances and these conditions may facilitate co-existence and maintain high diversity. To understand the influence of disturbances on vegetation, their spatial and temporal dimension, frequency of occurrence and magnitude has to be considered. Relatively more biological rich area was observed where the disturbance is low. The Indian Bureau of mines (2000) has recommended ecorestoration of dump as a part of natural succession process and it should be started with sowing of seeds of legumes, grasses, herbs and shrubs in the inter-spacing of tree plantation.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".