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Record W2392702579

Fuzzy distinguishing of the style of machine working mining for gently inclined thin seam

2000· article· en· W2392702579 on OpenAlex
Liang Chen

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

VenueJournal of Jiaozuo Institute of Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsCoal miningFuzzy logicIndex (typography)Mining engineeringStyle (visual arts)Set (abstract data type)Data miningCoalSelection (genetic algorithm)Computer scienceEngineeringArtificial intelligenceGeographyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

It has discussed the mining model selection effect on the geological condition when the apparatus are choosen on the inlimited condition. The geological index has contained multiple index and these index are some what fuzzy and not easily to determine. Based on the styles machine working and appropriate condition of severe coal getting machines for thin seam are analysed, the model of fuzzy distinguishing which can distinguish the style of coal mining for thin seam is set up. The experience of engineers and technicians can be studied by the model. According to the mining condition, the reasonable style of coal mining can be selected by the model.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.233
Teacher spread0.219 · 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