Phosphorus dynamics across intensively monitored subcatchments in the Beaver River
Bibliographic record
Abstract
We report results from a spatially intensive monitoring and modelling study to assess phosphorus (P) dynamics in the Beaver River, a tributary of Lake Simcoe, Ontario. We established multiple monitoring stations (9 flow and 24 water quality stations) from headwaters to near the outflow that were operated for 2 field seasons, complementing longer term data from a flow monitoring site and water chemistry monitoring site. We applied the Branched-INCA-P model, which allows fully distributed simulations supported by highly distributed monitoring data. Using spatially distributed data helped better understand variable P and sediment dynamics across the catchment and identify key model uncertainties and uncertainties related to catchment P management. Measured and modelled total P concentrations often exceeded provisional water quality thresholds in many areas of the catchment and highlight the value of studying water quality across multiple subcatchments rather than at a single site. Total P export coefficients differed widely among subcatchments, ranging from 2.1–21.4 kg km−2 y−1 over a single year. Export coefficients were most strongly (negatively) related to the proportion of wetland cover in subcatchments. The INCA-P model captured spatial variation in P concentrations relatively well, but short-term temporal variability in the observed data was not well simulated across sites, in part due to unmodelled hydrological phenomena including beaver activity and unknown drivers of P peaks that were not associated with hydrological events.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".