Bibliographic record
Abstract
The dataset includes information on each of the 459 operational coal mines in India such as 1) Mine name; 2) State name, 3) District name; 3) Coal production in 2019-2020; 4) Operator name; 5) Type of mine i.e. open cast or underground, among others. We obtained information on coal mines in India, their production, and location by filing applications under the Right to Information (RTI) with leading coal companies such as Coal India Limited its subsidiary companies, The Singareni Collieries Company Limited, and NLC India Limited, and the Coal Controller Organization (India’s coal sector regulator). The RTI Act in India is similar to the Freedom of Information Act in many other countries. The RTI replies were in the form of PDFs with details of the mines. We then individually entered the mine names in excel to create the dataset. PDFs can be made available upon reasonable request and on a case by case basis as the PDFs contain "personal data" of the researcher. (2020-11-26) (2020-11-26)
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.155 |
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; both teacher heads agree on what is shown here.
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".