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Record W2059566982 · doi:10.1080/09593332808618842

TREATMENT OF LOG YARD RUNOFF USING A RECIRCULATING SAND FILTRATION PROCESS

2007· article· en· W2059566982 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

VenueEnvironmental Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsSurface runoffTurbidityFiltration (mathematics)Environmental scienceSaturation (graph theory)CoatingParticle sizeEnvironmental engineeringAdsorptionChemistryHydrology (agriculture)Geotechnical engineeringGeologyEcologyMathematics

Abstract

fetched live from OpenAlex

A re-circulating filtration process using oxide-coated sand successfully removed COD and turbidity from log yard runoff. After passing only one pore volume of the runoff through the sand column, 72% COD was removed. The 2.4% Fe and Al oxide coating on the sand contributed to better COD removal than was obtained when the sand was stripped of oxide coating (86% versus 52%, respectively), at least initially before saturation of adsorption sites on the oxide coating occurred. The best COD removal performance came from conditioned sand. This sand, from the same original source and identical to the oxide-coated sand used in all experiments, came from an existing experimental sand column that had been treating log yard runoff for 1 year. The "conditioning" resulted in the sand having a higher TOC content (0.26% wt) and smaller particle sizes. This sand was able to consistently remove 80% COD from repeated batches of log yard runoff with strengths up to 3690 mg l(-1).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.530

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.223
Teacher spread0.214 · 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