Remote sensing of cyanobacterial blooms in Lake Champlain with a focus on Missisquoi Bay
Bibliographic record
Abstract
At 1270 km2, Lake Champlain, is the 13th largest lake in the United States. Lake Champlain experiences annual blooms of cyanobacteria, particularly in Missisquoi Bay. Here the Cyanobacteria Index, a previously published algorithm, was applied to satellite imagery from OLCI (available from 2016 through the present) and MODIS (available from 2000-present). The remotely sensed timeseries of the CI was derived and described from each sensor, along with cross-calibration among OLCI and MODIS. The resultant timeseries described when and where cyanobacteria blooms generally occur. Five distinct regions of Lake Champlain were considered for analysis: Malletts Bay, the Northeast Arm, Saint Albans Bay, Missisquoi Bay, and the Main Lake. Saint Albans and Missisquoi Bay were the only basins shown to have consistent cyanobacteria blooms. Saint Albans Bay, due to its small size was not an ideal fit for the methods used here, and the focus of this manuscript was Missisquoi Bay. The objective of this study is to explore the interannual variability of blooms in Missisquoi Bay and compare the variability to cyanobacteria blooms in Lake Erie, Saginaw Bay, and Green Bay. The blooms in Missisquoi Bay showed interannual variability in size, intensity, and start and end date. Observed data from the Burlington International Airport and modeled data from NASA’s Giovanni program were used in an effort to explain this bloom variability. A 2-parameter multiple regression model fit the cyanobacterial data well and showed that the interannual variability of blooms in Missisquoi Bay are a function of atmospheric instability and temperature.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".