Application of Multidimensional Statistical Methods to the Hydrochemical Study with R Software
Bibliographic record
Abstract
Conducting extensive, time-consuming analysis campaigns is a typical technique to better understand and manage surface water quality. These usually generate a substantial amount of data that is challenging to comprehend. Principal component analysis may be advantageous for such a project (PCA). From the perspective of such an application, eight physico-chemical parameters are important: Sodium (Na+), Bicarbonate (HCO3-), Magnesium (Mg2+), Total Alkalinity (as CaCO3), Chlorides (Cl-), Potassium (K+), Calcium (Ca2+), Sulfates (SO42-), coming from the analysis of 100 water samples collected between February 2014 and December 2015 on 25 stations distributed on Inaouen catchment areas, were analyzed. The principal component analysis applied to the data showed that the variables could be grouped into two principal components. The interpretation of the results using these tools allowed us to understand that the parameters responsible for water quality are related to component Dim1 (HCO3-, CaCO3, K+, Cl-, Na+ and SO42-) and component Dim2 to the processes associated to (Ca2+ and Mg2+) for the physicochemical parameters, the Dim1 factorial design accounts for 67.80% of the variance; it is expressed towards its positive pole by HCO3-, CaCO3, K+, Cl-, Na+ and SO42-, which present good correlations between them. However, the Dim2 factorial plane represents only 17.60%, defined by the Ca2+ and Mg2+ ions towards its positive pole. The Dim1XDim2 plane's typological structure reveals the individualization of three different groupings based on their hydrochemical quality. A feasible reduction in the number of dimensions without a major loss of information was discovered by the PCA. This tool is a good choice from the standpoint of developing management tools.
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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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.023 |
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".