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Record W4411618342 · doi:10.51847/xg65uncldk

10.51847/xg65UnclDK

2000· article· en· W4411618342 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTileInstitutionMechanism (biology)BusinessWork (physics)Operations managementEngineeringGeographyPolitical scienceArchaeologyLawMechanical engineeringPhilosophy

Abstract

fetched live from OpenAlex

Planning of organizational resources on providence chain management in glazed tile and earth ware industry can be effective.With the study of subject literature and main elastic indicators of investigation, planning of organizational resources is on providence chain management that its subordinate hypotheses like institution information and work group are being considered.With the attention to the statistical society that it is glazed tile and earthen ware of Yazd province, with snowball method, the sample number is chosen and we collected information by questionnaire.So inferential statics (factional analysis, direction analysis, structural analysis) is used for analysis of study data with SPSS and PLS soft wares.The result of data analysis showed that the hypothesis of group work is confirmed and institution elastic is rejected in glazed tile and earthen ware industry of Yazd province.Ultimately the acquired results gave some suggestions about glazed tile and earthen ware industry.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.9690.967

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 head0.004
GPT teacher head0.161
Teacher spread0.156 · 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

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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