Environmental and social predictors of phosphorus in streams on the island of Montreal, Quebec
Bibliographic record
Abstract
Phosphorus (P) is an essential nutrient for plant growth, however, in excess, it can pose a threat to water quality, most notably in freshwater systems. While researchers have focused on understanding the factors that influence stream P in non-urban areas, research on P dynamics in urban streams is lacking. Currently, urban development is the second-largest cause of stream impairment in the North America due, in part, to the impacts of nutrient pollution. For this reason, it is important to identify which commonly studied predictors of P in non-urban streams remain useful predictors in an urban setting and to determine whether characteristics unique to urban areas (i.e., socio-economic factors) can be used to predict P in urban streams. Seven streams on the island of Montreal were sampled daily to determine the P concentration in each. Stream flow was also measured in order to calculate the P flux in each stream. Stream P concentration and flux were compared to several physical and biological watershed characteristics that are commonly understood to be drivers of nutrient pollution, including percent impervious cover, land use, and amount of riparian buffer. Stream P concentration was also compared to several socio-economic watershed characteristics that I hypothesized would be good predictors in urban systems, including (average home value, median household income, fertilizer expenditures). Overall, two physical watershed characteristics (impervious surface cover and measures of land use) were most effective at explaining the variation in P concentration and P flux in the streams, while the biological and socio-economic variables were less effective. There is some evidence, however, to suggest that socio-economic variables (e.g. dollars spent on fertilizer per hectare of residential land) should continue to be examined with respect to urban stream P. After removing impervious surface cover as a predicto
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".