MétaCan
Menu
Back to cohort
Record W2474481190 · doi:10.2166/wqrj.2010.027

1995-2009: What Have We Learned About Effluent Biotreatment in Relation to Environmental Protection?

2010· article· en· W2474481190 on OpenAlexaffabout
Tibor Kovács, P. Martel, Sharon Gibbons, Valerie Naish

Bibliographic record

VenueWater Quality Research Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFPInnovations
Fundersnot available
KeywordsEffluentPulp and paper industryEnvironmental sciencePaper millFish <Actinopterygii>Waste managementEnvironmental engineeringFisheryEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract Since 1995, most mills in Canada have biotreatment in order to meet effluent regulatory limits for toxicity, biochemical oxygen demand, and total suspended solids. With occasional exceptions, the limits have been met. However, questions remain about effluent biotreatment regarding environmental protection, such as the reproductive capacity of fish. To address these concerns, a series of before-after studies were undertaken during the past decade. These included i) comparisons of effluents before and after biotreatment by means of fish (vitellogenin activity and egg production) and Ceriodaphnia (young production) tests done in the laboratory and ii) comparisons of fish communities in a river before and after the installation of effluent biotreatment at two mill sites. In all laboratory tests and with respect to all endpoints examined in these tests, the effects of the effluents after biotreatment were less or nonexistent when compared with the effects of the effluents before biotreatment. The assessment of the fish communities based on various metrics (e.g., percent piscivores, percent fish with anomalies) indicated improved conditions after the installation of biotreatment. Taken overall, the results indicated that biotreatment has improved effluent quality and this has resulted in clear improvements for the receiving environment.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.077
GPT teacher head0.364
Teacher spread0.287 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueWater Quality Research JournalSame topicFish Ecology and Management StudiesFrench-language works237,207