Bibliographic record
Abstract
This work focuses on the advanced physical analysis of stormwater sediments using laser diffraction particle size analyzer and scanning electron microscope and chemical characterization using neutron activation analysis. Since previous studies of sediments from stormwater ponds in the Greater Toronto Area indicated a marginal-to-significant level of pollution by most of the regulated heavy metals, the characteristics of the storm water sediment samples obtained in this study were compared to the sediment quality guidelines of the Ontario Ministry of the Environment. The images from optical microscope showed that the particles in the dried sediment were irregular, and the sizes of each particle vary greatly. Using scanning electron microscope, it was shown that two different structures of particles were present in the storm water sediment. It was also observed that the main compositions (above 1000ppm) of the dried sediment included, in descending order of concentration, Ca >AI> Fe> K > Mg > Na >Ti > Mn. The trace compositions (below 1000ppm) included, also in descending order of concentration, Cl > Zn > Ba > Sr > Cr > V > La > Nd > As > Br > Co > Sc > Th > Sb > Sm > Eu. The concentrations of regulated elements such as Cr, Fe Zn, As and Mn were above the lowest effect level, suggesting that treatment of stormwater sediment may be necessary. A preliminary stormwater sediment treatment experiment using thermal plasma technology was therefore conducted. After the thermal plasma treatment, the percentage of total organic carbon decreased and eight gas compounds including CO, COz, NO, NOz, NOx, SOz, H2S and CxHy emitted during the process. Enrichments of Mg, Cl and Na were observed in the treated sludge while the concentrations of K and Ca decreased. The potential of thermal plasma technology for the treatment of contaminated stormwater sediment was demonstrated.
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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 | 0.000 | 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) | 0.000 | 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".