Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria
Bibliographic record
Abstract
Haig HA, Chegoonian AM, Davies J-M, Bateson D, Leavitt PR. 2021. Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria. Lake Reserv Manage. 38:1–15.Continued eutrophication of inland waters by nutrient pollution can combine with unprecedented atmospheric and lake warming to create emergent environmental surprises. Here we report the first known occurrence of marked blue discoloration of ice and water in highly eutrophic prairie lakes during late winter 2021. Intense blue staining was reported first to governmental agencies by ice fishers in early March 2021, then communicated widely through social media, resulting in First Nations and public concern over potential septic field release, toxic spills, urban pollution, and agricultural mismanagement. Analysis of water from stained and reference sites using ultraviolet (UV)–visible spectrophotometry and high-performance liquid chromatography demonstrated that the blue color arose from high concentrations (∼14 mg/L) of the cyanobacterial pigment C-phycocyanin that was released after an unexpected bloom of Aphanizomenon flos-aquae in late October 2020 was frozen into littoral ice. Remote sensing using the Sentinel 3 A/B OLCI and Sentinel 2 A/B MSI satellite platforms suggested that blue staining encompassed 0.68 ± 0.24 km2 (4.25 ± 1.5% of lake surface area), persisted over 4 weeks, and was located within 50 m of the lakeshore in regions where fall blooms of cyanobacteria had been particularly dense. Although toxin levels were low (∼0.2 μg microcystin/L), high concentrations of C-phycocyanin raised public concern over eutrophication, pollution, and climate change, and resulted in rapid governmental and academic response. Given that climate change and nutrient pollution are increasing the magnitude and duration of cyanobacterial blooms, blue staining of lake ice may become widespread in eutrophic lakes subject to ice cover.
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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 | 0.000 |
| 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".