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Record W2312769005 · doi:10.1061/41114(371)66

From Problem Statement to Pilot Scale: A Hybrid Passive Treatment System for Landfill Leachate in North Bay, Ontario, Canada

2010· article· en· W2312769005 on OpenAlexaffabout
Sean Speer, Pascale Champagne, Bruce C. Anderson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeachateEnvironmental scienceWaste managementMunicipal solid wasteEnvironmental engineeringBayCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Passive treatment of landfill leachate is an enticing option for landfill sites since efficient treatment of the leachate generated can continue after the landfill closes and is no longer receiving waste (therefore not generating a profit). Treatment performance in these systems is affected by regional climate and therefore site-specific research needs to be considered for each treatment system design. This three-phase treatability study was designed to determine a pretreatment system that would minimize the size of the subsequent passive treatment system, and to evaluate various passive treatment technologies at both the bench and pilot scales. The results from this treatability study will be used to support the use of passive treatment systems for landfill leachate mitigation in North Bay, as well as serving as a template for other sites in the northern Ontario climate looking to implement passive treatment of landfill leachate.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.479

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2010
Admission routes2
Has abstractyes

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