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Record W2898503720 · doi:10.3390/proceedings2211365

Adapting to Engineering Education Vision 2020

2018· article· en· W2898503720 on OpenAlexaff
Musarat Yasmin, Farhat Naseem, Malik Hassan Raza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)Multidisciplinary approachEngineering ethicsEngineeringSociologySocial science

Abstract

fetched live from OpenAlex

Interdisciplinary energy research has become inevitable in the context of perceived energy break-point after 2050. Power and energy crisis is a matter of life or death for industry and human race on earth. Oil and natural gas peaking alarms started ringing by the start of the 21st century. Available energy reserves are emptying at of thousands of barrels per second and time to discover new energy sources is being wasted to convince and advocate disciplinarians going for interdisciplinary research approach. We will have to invent new ways of supplying 30% of the global energy demand by 2030 and 60% by 2050. It is not possible without putting the emerging bio, nano, and info technologies together in power and energy research laboratories under interdisciplinary and trans-disciplinary approaches. Electrical engineers badly need the supportive hand of energy scientists and technologists to overcome global power, energy, food, and water crises. Engineers and scientists often find it difficult to tolerate each other and usually end up with duplicate resources without any presentable output which requires motivation to develop teamwork spirit to succeed. This paper unveils the potential urgency for an interdisciplinary research approach concerning embedded energy research barriers and solutions in developing countries. Enhancing power and energy multidisciplinary research is a vital general formula that can be tailored to specific regional conditions to minimize the greenhorn blues to run local and global interdisciplinary research programs.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.712

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)0.0000.001

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 head0.003
GPT teacher head0.215
Teacher spread0.212 · 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 designNot applicable
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

Citations5
Published2018
Admission routes1
Has abstractyes

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