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
Laboratorio de Ecología Funcional y Ecosistemas Tropicales (LEFET), Escuela de Ciencias Biológicas, Universidad Nacional, Costa Rica, 86-3000 Laboratorio de Fitotecnología (LAFITOTEC), Escuela de Ciencias Biológicas, Universidad Nacional, Costa Rica, 86-3000 Laboratorio de Gestión de Desechos y Aguas Residuales (LAGEDE), Escuela de Química, Universidad Nacional, Costa Rica, 86-3000 Laboratorio Nacional de Aguas, Área de Microbiología, Instituto Costarricense de Acueductos y Alcantarillados (AyA), Costa Rica, 30306 * Correspondence: junior.perez.molina@una.cr 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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".