Nutrient Loss Rates in Relation to Transport Time Scales in a Large Shallow Lake (Lake St. Clair, USA—Canada): Insights From a Three‐Dimensional Model
Bibliographic record
Abstract
Abstract A nutrient mass balance and a three‐dimensional, coupled hydrodynamic‐ecological model, calibrated and validated for Lake St. Clair with observations from 2009 and 2010, were integrated to estimate monthly lake‐scale nutrient loss rates, and to calculate 3 monthly transport time scales: flushing time, water age, and water residence time. While nutrient loss rates had statistically significant relationships with all transport time scale measures, water age had the strongest explanatory power, with water age and nutrient loss rates both smaller in spring and fall and larger in summer. We show that Lake St. Clair is seasonally divided into two discrete regions of contrasting water age and productivity. The north‐western region is dominated by oligotrophic waters from the St. Clair River, and south‐eastern region is dominated by the nutrient enriched, more productive waters from the Thames‐Sydenham River complex. The spatial and temporal variations in local transport scales and nutrient loss rates, coupled with strong seasonal variations in discharge and nutrient loads from the major tributaries, suggest the need for different load reduction strategies for different tributaries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".