ANÁLISES DESCRITIVAS E MICROBIOLÓGICAS DAS ÁGUAS MINERAIS ENVASADAS E COMERCIALIZADAS NA REGIÃO METROPOLITANA DE RECIFE-PE.
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.
Bibliographic record
Abstract
The paper aims to describe the quality of mineral water marketed by the population of the metropolitan area of Recife-PE in 2015, regarding the microbiological and descriptive analyzes. We analyzed 70 samples of seven different brands of bottled mineral water in the period from January to April and June to August due year. The samples were divided into thirty-five units for both periods, according to Standard Methods for the Examination of Water and Wastewater, by means of testing the presence or absence (P-A) and Pour Plate Method. Regarding the microbiological analysis of samples of the first period in accordance with Resolution 275/2005 brands A, B and C had their departures REJECTED samples and the marks D, E, F and G were APPROVED. In the second period the marks A, B, C, D and E had their departures REJECTED samples and the F and G brands were APPROVED. The percentage shown in the first period indicates 57.14% (APPROVED) and 42, 85% (REJECTED). In the second period the percentage indicates that 28.75% (APPROVED) and 71.42% (REJECTED). That is, in the second period there was obtained a high percentage of water which has been rejected due to the presence of microbiological bacteria.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it