Bridging the gap between terrestrial, riverine and limnological research: Application of a model chain to a mesotrophic lake in North America
Bibliographic record
Abstract
Models remain our best available tool for managing low lake dissolved oxygen concentrations, which pose a serious ecological risk. This study investigated whether process-based catchment models (INCA-N and INCA-P) could accurately drive a lake model (PROTECH), to bridge a gap between terrestrial, riverine and limnological research. INCA was calibrated over all 20 catchments of the Simcoe watershed, Canada. Daily outputs (flow, nitrogen and phosphorus concentrations) over the period 2010–2016 were selected for a common “baseline” period, and used as inputs to PROTECH, which was calibrated across the three major basins of lake Simcoe; Kempenfelt (K42), Cooks (C9), and the main basin (E51). Results showed that at catchment outflows INCA models achieved an average flow R2 of 0.8; a load R2 of 0.7 (both for TP and N-NO3), a concentration R2 of 0.4 and 0.5 (for TP and N-NO3 respectively), and an SiO2 R2 > 0.8. In each basin PROTECH achieved an R2 for both temperature and dissolved oxygen (DO) concentrations of > 0.9. Performance of N-NO3, TP and Chlorophyll-a concentrations were good (R2 values of up to 0.98, 0.92 and 0.53 respectively). Multi-stressor analyses established that most occurrences when DO dropped below the desired 7 mg/l threshold (DO7) were attributable to combinations of high temperatures and low tributary inflows. The importance of additional drivers was depth dependent, with photosynthesis being particularly important in shallower C9 and E51 basins during summer, when algae contributed sufficient O2 to the water column to inhibit DO7 events. Conversely in the deeper more strongly stratified K42 basin, greater algal growth boosted the biochemical oxygen demand, enhancing declines in DO. Lake physics explained a significant number of DO7 events in all three basins. Integrated catchment-lake modelling approaches are important in understanding lake physical and ecological processes, and the impacts of land management and future climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".