Additional file 1 of Assessment of stormwater discharge contamination and toxicity for a cold-climate urban landscape
Bibliographic record
Abstract
Additional file 1: Table S1. Limits of detection (LOD) and quantification (LOQ) for polyaromatic hydrocarbons. Table S2. Land use classifications used to calculate runoff volumes in this study based on Järveläinen et al. [39]. The runoff coefficients (CR) considered in the current study follow City of Saskatoon (CoS) stormwater management guidelines. Table S3. Average flow-weighted site mean concentration (SMC) values for different land use classes, from Melanen (1981), Mitchell (2005), Nordeidet et al. (2004) and Järveläinen et al. [39] as adapted by Al Masum et al. [3]. The SMC is the geometric mean of the event mean concentration for each storm event, which is the concentration of pollutants as a function of the runoff volume discharging in the river (flow-weighted). The value is used to estimate overall SW contaminant loading over a given urban area. Values in parentheses are standard deviations (SD). Table S4. Overview of analyzed stormwater quality parameters for the 2019 sampling season grouped by site. Catchments outlined in Additional file 1: Figure S1. Values are average (standard deviation, SD). Quality parameter abbreviations are as follows: total dissolved solids (TDS), electrical conductivity (EC), dissolved organic carbon (DOC), chemical oxygen demand (COD), and total suspended solids (TSS). Table S5. Overview of stormwater quality parameters for the 2019 sampling season for each individual event and outfall. Catchments outlined in Additional file 1: Figure S1. Table S6. Chloride and sulphate analysis for select 2019 stormwater samples. Table S7. Metals (µg/L) detected in 2019 stormwater samples. Table S8. PAHs (ng/L) detected in 2019 stormwater samples. NM not measured. Table S9. Theoretical seasonal loading estimates for various physicochemical parameters of interest. Estimates in this table are based on theoretical SMC values given in Additional file 1: Table S3. Rainfall depths used to estimate seasonal catchment runoff volumes are included in Additional file 1: Table S4. Estimates using measured seasonal mean concentrations are included in Additional file 1: Table S10. Table S10. Correlations between runoff volume from land use area across each catchment (calculated from surface area; see “Methods” section) and contaminant loading. Loading is based upon averages of measured 2019 SW concentrations; see Additional file 1: Table S5. Figure S1. Stormwater catchments delineated within the CoS following Al Masum et al. [3]. Catchments included in this study are Light & Power (SCB E), Circle Drive S Bridge (SCB W), 14th St. E. (including both the MacPherson Ave. and 14th St. E. catchments), 17th St. W., 23rd St. W., and Wanuskewin Road (Silverwood Dog Park). Stormwater sampling occurred over summer 2019; the data are intended to complement a parallel 2018 study examining summer SW quality in the Taylor Street, Avenue B S, Preston Crossing, Dog Park (Preston), Spadina/Sturgeon, and Whiteswan/WWTP catchments. Figure S2. Land use breakdown of study catchments. The 14th St. E. catchment above drains to both the MacPherson Ave. and 14th St. E. outfalls (though not delineated on the land-use map, areas for each subcatchment are provided by the CoS). Adapted from Al Masum et al. [3]. Refer to Additional file 1: Table S1 for land use classification acronyms. Refer to Additional file 1: Table S2 for total and land-use surface area in km2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.838 | 0.143 |
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 source (direct Gemma or distilled Codex), 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".