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Record W7100514653

virtual and physical environment

2014· article· en· W7100514653 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTroubleshootingProcess (computing)AutomationContext (archaeology)Work (physics)Production (economics)ProductivityControl (management)
DOInot available

Abstract

fetched live from OpenAlex

Nowadays, industrial software and communication tools allow the managerial staff, engineers and technicians to enhance the productivity and optimize automated production systems without the necessity of being physically on the production floor. In some instances, equipment providers can even assist plant engineers and technicians in troubleshooting specific equipments from a distant site, using advanced tools providing real-time information (visual, process data, etc.) from the plant. The current project, through the use of recent industrial communication technologies, proposes the reproduction of a similar situation, in joint learning activities for future engineers and technicians. This kind of interaction, in an early educational context at both levels, is unique to the knowledge of the authors. The institutions taking part in this project are the Université du Québec à Rimouski (UQAR) and the Cégep de Rivière-du-Loup, located in eastern Quebec. Two activities were initiated, the first as part of a process control course using a stand-alone physical setup, and the second using a currently designed mini-plant for recycling beverage bottles and cans as part of a sequential automation course. Results showed that the students at both institutions were able to work together and communicate effectively despite their different background. The training objectives for this phase of the project were successfully achieved and lessons learned will be further exploited to enhance the training level and student-acquired skills during the following phase of the project.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.190

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)0.0000.000

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 head0.181
Teacher spread0.168 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2014
Admission routes1
Has abstractyes

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