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Record W4398954746 · doi:10.7910/dvn/tdek8o

Indian coal mine location and production - December 2020

2021· dataset· en· W4398954746 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHarvard Dataverse · 2021
Typedataset
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoal miningMining engineeringProduction (economics)CoalGeologyGeographyEnvironmental scienceArchaeologyEconomics

Abstract

fetched live from OpenAlex

We obtained information on coal mines in India, their production, and location by filing applications under the Right to Information (RTI) Act, 2005 with leading coal companies such as Coal India Limited & its subsidiary companies, Singareni Collieries Company Limited and NLC Ltd. We also filed these applications with the Indian Coal Ministry and the Coal Controller Organization (India’s coal sector regulator). The RTI Act in India is the similar to Freedom of Information Act in many other countries. Most RTI replies were in PDF format with details of the mines. We individually entered the mine names and other details 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. The geocoordinates were created by researchers using latitude/longitude data using published in various government documents or using GoogleMaps. Most of the coordinates are exact but some are approximate but within the same local region as the exact coordinates were not available.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.003
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.004

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.

Opus teacher head0.011
GPT teacher head0.208
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it