AVALIAÇÃO DO REL - APLICATIVO DE RELACIONAMENTOS COM OS CLIENTES SEGMENTADOS DO BANCO DO BRASIL, NA REGIÃO DA ÁGUA VERDE EM CURITIBA-PR
Bibliographic record
Abstract
To recognize and identify the real necessities of the employees in the use of an application to minimize the possible imperfections, indispensable factor to get a market advantage increasing the expectations of the customers and the organization, consequently, searching performance improvements. The subject in this work mentions evaluation to make for the employees of the Green Water, quarter agency, in Curitiba-Parana, on the REL – it was applicated to the customers relationships of the Banco do Brasil. The present study has as objective to evaluate the application of its use, where we look for to identify the main proportionate benefits for its use, which information are more used by the employees, to identify the main strong and weak points of the application, and possible special assessments. The work is presented of descriptive form, telling resulted of research got through the instrument of collection of data. The research is qualitative and was carried through with 15 employees of the Green Water agency. The study was a research, therefore it has the intention to develop concepts that they aim to the elaboration of necessary problems or intentions of future research, also classified as a former-post study fact, a time that it looked to discover facts of relationships between the 0 variable, after the phenomenon in study have already occurred.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".