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Record W2062001257 · doi:10.5539/jgg.v1n2p28

Factor Analysis-Based Optimal Selection of Rock-Breaking Bit Applied in Deep Layer of Songliao Basin

2009· article· en· W2062001257 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Geography and Geology · 2009
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBit (key)Structural basinFactor (programming language)DrillingSelection (genetic algorithm)GeologyLayer (electronics)Rotation (mathematics)Computer scienceGeotechnical engineeringPetroleum engineeringMathematicsStatisticsArtificial intelligenceEngineeringGeomorphologyMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

Correctly choosing rock-breaking bit is critical in oil drilling. The optimal model of selecting a bit has been establishedbased on the Factor Analysis Theory. Through selecting primitive variables, and using SPSS (Statistical Package for theSocial Sciences) to get factor loading matrix, factor rotation and factor score, we have reasonably evaluated andoptimized the selection of rock-breaking bits applied in deep layer drilling of Songliao Basin. The calculated results areconsistent with the observation from actual applications. It indicates that the method studied here is reasonably reliableand valuable for broader applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.338
Teacher spread0.311 · 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