Long term water clarity changes in<scp>N</scp>orth<scp>A</scp>merica's<scp>G</scp>reat<scp>L</scp>akes from multi‐sensor satellite observations
Bibliographic record
Abstract
Abstract Water clarity in North America's Laurentian Great Lakes has undergone considerable change over the last several decades as a consequence of invasive species, eutrophication, and implemented nutrient management practices. Satellite observations from the CZCS, SeaWiFS, and MODIS‐Aqua sensors have been used in tandem with long term records of Secchi disk depth (ZSD) to provide a retrospective analysis of spatial and temporal variations in water clarity over the Great Lakes. A simple empirical algorithm is presented, relatingZSDto remote‐sensing reflectance at ∼ 550 nm (Rrs∼550). Results suggest remarkable and complex changes in water clarity over the Great Lakes. Lakes Ontario, Huron, and Michigan have seen increases in averageZSDover the three sensor periods of 58%, 49%, and 62%, respectively. Lake Erie shows highly variableZSDwith no consistent long term trends, while Lake Superior has remained fairly consistent in its lake‐wide water clarity conditions. Temporal trends document the decrease in whiting events on Lake Michigan while capturing the ongoing occurrence of these seasonal bright‐water events on Lake Ontario. Results indicate a divergence inZSDtrends between nearshore and offshore environments; with larger increases in offshore than nearshoreZSDand some nearshore areas suggesting a decrease inZSD. Offshore regions of Lakes Huron, Michigan, and Superior show diminishedZSDseasonality in contrast to increasing seasonal variance in the nearshore. Spatial and temporal variations inZSDare in agreement with documented reductions in Great Lakes bioproductivity, degrading nearshore water quality, and changing biogeochemical processes influencing whiting events and sediment resuspension.
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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 | 0.000 |
| 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) | 0.001 | 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".