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Record W2743388006 · doi:10.2495/sdp-v12-n8-1359-1369

Proactive management in the power industry: Tool support

2017· article· en· W2743388006 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

VenueInternational Journal of Sustainable Development and Planning · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)BusinessProcess managementEngineering

Abstract

fetched live from OpenAlex

Increasingly dynamic, non-linear and all too often chaotic changes in the global environment and tougher competition, including at the geopolitical level, call for radical transformations in strategic management of the power industry. The article provides the results of a study into proactive actions of energy company management, which are becoming increasingly important. The article offers a framework of concepts relating to proactive management and sums up ideas of a number of authors on diagnostics of weak signals as possible harbingers of threats to sustainable power industry development. The authors have determined a general approach to mechanisms of threat identification and developed methodological principles of shaping a corporate management model capable of reacting to new challenges. The article provides a thorough study of some components of the model and an assessment of factors that ensure successful implementation of the authors' conceptual solutions in energy companies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.228

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 head0.016
GPT teacher head0.270
Teacher spread0.255 · 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