Coastal Upwelling Influences Hypoxia Spatial Patterns and Nearshore Dynamics in Lake Erie
Bibliographic record
Abstract
Abstract Hypoxia, defined as dissolved oxygen (DO) < 2 mg/L, in the central basin of Lake Erie has been studied since the mid‐1900s. Even so, spatial patterns of hypoxia, and episodic hypoxia in nearshore areas where drinking water plant intakes are located, are not well characterized owing to limited observations and short‐term dynamics. We evaluated a physically based, DO model with respect to patterns of hypoxia observed in Lake Erie. The DO model used assigned rates of sediment and water column oxygen demand that were temperature dependent but otherwise spatially and temporally uniform. The DO model was linked to National Oceanic and Atmospheric Administration's (NOAA) Lake Erie Operational Forecasting System hydrodynamic model, an application of the Finite Volume Community Ocean Model (FVCOM). Model temperature and DO were compared with observations from ship‐based studies, real‐time sensor networks and an array of moored sensors that we deployed in 2017. In years with dominant southwesterly winds, persistent downwelling occurred along the south shore, which resulted in a thinner thermocline and earlier initiation of hypoxia along the south shore than the north. Occasional northeast winds temporarily reversed this pattern, causing upwelling along the south shore that brought hypoxic water to nearshore locations and water intakes. The DO model reproduced observed spatial and temporal patterns of hypoxia and revealed locations subject to episodes of hypoxia, including nearshore Ohio, north of Pelee Island, and near the Bass Islands. Model skill was limited in some respects, highlighting the importance of accurate simulation of the thermal structure and spatial patterns of oxygen demand rates.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".