Spatial variation of physicochemical parameters in a constructed wetland for wastewater treatment: An example of the use of the R programming language
Bibliographic record
Abstract
<sup>Laboratorio de Ecología Funcional y Ecosistemas Tropicales (LEFET), Escuela de Ciencias Biológicas, Universidad Nacional, Costa Rica, 86-3000</sup> <sup>Laboratorio de Fitotecnología (LAFITOTEC), Escuela de Ciencias Biológicas, Universidad Nacional, Costa Rica, 86-3000</sup> <sup>Laboratorio de Gestión de Desechos y Aguas Residuales (LAGEDE), Escuela de Química, Universidad Nacional, Costa Rica, 86-3000</sup> <sup>Laboratorio Nacional de Aguas, Área de Microbiología, Instituto Costarricense de Acueductos y Alcantarillados (AyA), Costa Rica, 30306</sup> <sup>* Correspondence: junior.perez.molina@una.cr</sup> This WTS-project in R aimed to evaluate the spatial variation of some of the physicochemical parameters in an constructed wetland system for wastewater treatment of sub-superficial flow of "Pennisetum alopecuroides" (Pennisetum) and a control (unplanted). The purpose is to provide a simple example of an analysis of the spatial dynamics through the use of the R programming language. Each of the cells (Pennisetum and control) had 12 piezometers, organized in three columns and four rows with a separation distance of 3.25m and 4.35m, respectively. It was measured in each of the piezometers the oxidation-reduction potential (ORP), dissolved oxygen (OD), conductivity, pH and water temperature (n = 167). The monitoring of the spatial variation of these parameters and other variables could show us if there is any obstruction of the flow and/or possible reduction of the removal by the plants. An open-source repository of R was provided.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".