Characterization of Particles in Fresh and Primary-Treated Log Sort Yard Runoff
Bibliographic record
Abstract
Abstract Runoff from three southwest British Columbia (B.C.), Canada (Sunshine Coast), log sort yards was characterized to determine the colloidal and particulate fraction structures and the distribution of organic and metal constituents. Runoff from these sites, resulting from rainfall and on-site sprinkling, contains suspended and colloidal particles that are largely organic. At one log sort yard, the runoff receives primary treatment in a lagoon, whereas at the other sites, at the time of the study, runoff was directly discharged into the aquatic environment. The fresh runoff contained a high strength of organic compounds as determined by chemical oxygen demand (COD) analyses, which ranged from 346 to 3690 mg L-1. For a rainfall-generated runoff sample, particulates (particles greater than 1–2 µm) contributed up to 52% of the total COD and colloids (particles between 20 nm and 1–2 µm) 39%. Following primary treatment in the lagoon, organic compounds present were mostly colloidal. In both samples (fresh and primary-treated) zinc and aluminum concentrations exceeded the B.C. Approved Water Quality Guidelines. Primary treatment experiments revealed that 27 to 54% of the COD could be removed by settling, depending on the initial concentration. An additional 33% of COD was removed, probably due to biological degradation during the settling time. Chemical oxygen demands of the final treated effluents remained relatively high (378–533 mg L-1) showing that not all the suspended material could be removed through settling and biodegradation and that other treatments are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".