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Record W2064271429 · doi:10.1081/pfc-100103574

TREATMENT OF GREENHOUSE WASTEWATER USING CONSTRUCTED WETLANDS

2001· article· en· W2064271429 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWetlandEnvironmental scienceEffluentWastewaterGreenhouseSubsurface flowSewage treatmentNitrateEnvironmental engineeringPhosphorusConstructed wetlandSurface waterAgronomyEcologyGroundwaterChemistryEngineeringBiology

Abstract

fetched live from OpenAlex

Five wetland designs, based on conventional surface flow (SF) and subsurface flow (SSF) approaches, were assessed for nitrogen and phosphorus removal from greenhouse wastewater. Results indicated none of the individual designs assessed was capable of providing the highest treatment effect for all nutrients of concern; however, the SF wetland emerged as the most appropriate design for the treatment of greenhouse wastewater. The highest mean phosphorus reduction of 65% was observed in the unplanted SF wetlands. Peak nitrate reductions of 54% were observed in the 15-cm deep SF wetlands and ammonia removal of 74% was achieved in the unplanted SF wetlands. Nitrate concentration in the greenhouse effluent can be reduced to acceptable levels for the protection of freshwater aquatic life (i.e., less then 40 ppm) using a loading rate of 1.65 g NO3-N/m2/day and a design water depth of 30 cm or greater. Based on available literature and the results of this research project, a multistage design, consisting of an unplanted pre-treatment basin followed by a 25 to 35 cm deep surface flow marsh with open water components, is recommended.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.276
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it