Business Intelligence in Decision Support Focusing on Collective Continuity Indicators
Bibliographic record
Abstract
The expansion of organizations demands more and more information as an input to acquire greater control of activities, and for the treatment of this data. The information technology scenario has a growing and sharp curve, especially in the scope of big data, requiring attributes for analysis and compilation of the data obtained, thus ensuring the provision of information in a timely manner for more accurate and assertive decisions about the future of organizations. Thus, the present study seeks to show how the implementation of a Business Intelligence tool impacts on collective indicators of continuity of electricity supply. Energy is one of the main inputs for organizations and for everyone who depends on it. Its availability allows a guarantee of the continuity of socio-economic development. Therefore, the objective of this was to carry out descriptive research with a quali-quantitative approach through an applied study, having as locus an electric energy distribution concessionaire, approaching the scenarios before and after the implementation of the tool, making it possible to highlight the improvements in the organization through Business Intelligence. It also has an approach regarding the aid in the decision-making process through this tool, and consequently its contribution to the process of continuous improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".