A validation workflow for treatment wetland performance data
Bibliographic record
Abstract
ABSTRACT Treatment wetlands (TWs) effectively remove target pollutants and enhance urban water circularity and resilience. They constitute a prominent solution for urban wastewater treatment, thanks to their adaptability across various types of wastewater, scales and climatic conditions. However, the disparity in TW designs and the focus on a restricted set of variables applicable to research studies impede any comprehensive evaluation and comparison of TW performance. Our study introduces a methodology for data validation, in concurrently establishing a workflow specific to TW. This approach is aimed at defining the scope and relationships within the data, implementing checks and concatenating them into a quality flag, as an initial step towards building reliable statistical models. We underscore the importance of both mobilising comprehensive knowledge and identifying customary, yet implicit, choices intertwined in data processing. As for the application workflow, we collected and analysed data sourced from peer-reviewed papers on horizontal and vertical flow TW. Deficiencies were noted in key data elements like dimensions, concentrations and operational conditions. For the data analysis, relationships are highlighted between variables introduced for modelling purposes. These methodologies and workflows assess the quality of the data, in paving the way towards more dependable statistical models for TW design and implementation.
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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 | 0.098 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".