Assessment of water quality using principal component analysis: a case study of the Marrecas stream basin in Brazil
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
Monitoring water quality is a fundamental process to ensure proper anthropogenic usage and environmental protection of this resource. This study collected monthly measurements of 9 parameters (pH, Temperature, BOD, Total Solids, Thermotolerant Coliforms, Dissolved Oxygen, Total Nitrogen and Total Phosphorus) in 5 sampling stations along the Marrecas water stream, during a 1-year period. Temporal and seasonal variations were analyzed and interpreted for each element, explaining how specific geographical and anthropogenic factors affected the water body. Principal Component Analysis (PCA) was applied to evaluate each element's correlation and to reduce the number of parameters, easing the assessment of water quality for each location. Results were followed by the creation of an improved index for the region, which could better estimate the quality of water, only considering 4 of the original parameters. It was also recognized that each water body possesses several subtleties that impact on how its water quality should be measured and indexed into a single value, which validates the case for the creation of regional WQI's.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it