Development of a Model with Critical Factors of Success, Predominant in Implementation of a Membrane System in the Wastewater Treatment—Review of the Case Study of a Dairy Industry
Bibliographic record
Abstract
The objective of this study was to identify the critical success factors (CSFs) that predict, in a specific niche, the dairy sector in Brazil and later monitor their behaviors behavior when applied together with the project management activity, along with a case study where the extrapolation of the implementation of wastewater treatment by the combined membrane system was carried out. In order to develop the case, the hypothetical-deductive method was adopted and later the content analysis was carried out through the Sphinx Lexical (qualitative analysis) computer system, data clusters and quantitative data validation was performed with SPSS Statistic, allowing to understand CSF of classification. In applying this methodology, after grouping factors in the company, one can verify the existence of implicit relationships of the FCSs, impacting mainly on the organizational aspects, especially related to the effective communication and the need for managerial support in the decision making as the most representative and factors related to risk planning and analysis. As for the explicit impact of the factors with the organization, one can verify the predominance of cost factors, and the possibility of reusing water.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".