Widespread prevalence of hypoxia and the classification of hypoxic conditions in the Laurentian Great Lakes
Bibliographic record
Abstract
Aquatic hypoxia within the Laurentian Great Lakes has contributed to various adverse ecological consequences and stimulated research interest in recent decades. An analysis of published peer-reviewed journal articles from 2000 to 2020 demonstrates an increasing trend of studies related to hypoxia in the Laurentian Great Lakes. However, the majority of these studies (78%) focus on Lake Erie and in particular the well-documented hypolimnetic hypoxic conditions that develop in the central basin of Lake Erie. This hypoxic zone is relatively large (up to 1.5 million ha), has substantial ecological effects, and motivates monitoring programs and water quality improvement initiatives. Nonetheless, the hypoxic zone in the central basin of Lake Erie is only one of over twenty documented hypoxic zones in the Laurentian Great Lakes. Moreover, hypoxic conditions in the Great Lakes are quite diverse. Here, we define and characterize a four-fold classification of Great Lakes hypoxic conditions: 1) hypolimnetic hypoxia, 2) over-winter hypoxia, 3) diel hypoxia, and 4) episodic hypoxia. We suggest that Great Lakes research and monitoring programs should seek to more broadly document hypoxic conditions and develop models to predict the temporal and spatial occurrence of hypoxia. Such efforts are particularly timely as future climatic conditions contributing to warmer temperatures, longer and more intense stratified periods, increased spring nutrient loading and more variable allocthonous inputs are expected to exacerbate three of the four hypoxic conditions described for the Great Lakes (hypolimnetic, diel, and episodic hypoxia).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".