Bibliographic record
Abstract
During the last ten to 15 years, wet stormwater management ponds have become an increasingly popular best management practice in Ontario.The performance of a stormwater management pond is time dependent and steadily decreases as sediment accumulation occurs.To remain effective, ponds need to be regularly monitored and sediment must periodically be removed.However, the vast majority of Ontario ponds are not monitored for performance and have yet to be dredged for the removal of accumulated sediment.It is imperative that the costs and execution procedures of pond clean-outs be addressed if stormwater management ponds are to continue to provide effective stormwater management.In this study, the state-of-practice regarding the maintenance of wet stormwater ponds in Ontario is surveyed and analyzed.Rsum : Pendant les dernires 10 15 annes, les tangs de gestion de prcipitation exceptionnelle sont devenus de plus en plus la manire d' tang de gestion de prcipitation la plus populaire utilis chez les municipalits de l'Ontario.La performance d'un tang de gestion de prcipitation exceptionnelle dpend sur le temps et diminue lorsque l'accumulation de sdiment se produit.Ainsi pour pouvoir continuer d'tre efficace, les tangs doivent continuer d'tre surveills rgulirement et les sdiments doivent tre priodiquement enlevs.Cependant, la vaste majorit d'tangs en Ontario ne sont surveills pour leur performance et doivent encore tre nettoys.C'est impratif que le cot et procdures d'excution du nettoiement des tangs doivent tre adress si les tangs de gestion de prcipitation exceptionnelle prouvent de continuer d'tre effectifs face aux tangs de gestion.Dans cette tude, l'tat de pratique face maintenir les tangs de gestion de prcipitation exceptionnelle en Ontario sont surveills et analyss.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".