Investigating Possible Effects of Changing Hydroclimatic Persistence on Lake Cyanobacterial Harmful Algal Blooms
Bibliographic record
Abstract
Harmful Algal Blooms (HABs) are a major environmental problem worldwide. Apart from their adverse effects on aquatic habitat, and possible economic losses, they also pose a serious threat to public health. Future climatic uncertainties that include possible shifts in patterns of climatic variables are points of concern in terms of how such changes would affect the development, growth, and duration of HABs. Weather whiplash, abrupt dry-to-wet or wet-to-dry condition transitions, is one of these shifts in climatic patterns and despite its potential environmental impacts, few studies have examined the implications of such changes on lake water quality. Lake Champlain, located on the US-Canada border, has repeatedly faced cyanobacterial HABs predominately in its shallow bays. The aim of the current work is to (i) investigate potential changes in the persistence of hydroclimatic variables (precipitation and temperature) and (ii) examine their effects on cyanobacterial HABs in the lake’s shallow Missisquoi Bay. Our approach focuses on short-term persistence (STP) shifts over different timescales (daily, monthly, seasonal, and annual). STP scenarios that capture these plausible shifts are constructed using projected climate scenarios for the period 2000-2040. An Integrated Assessment Model that simulates the Missisquoi Basin’s physical processes, including watershed hydrology, management, and the Missisquoi Bay’s water quality dynamics, is utilized to run the modeled STP scenarios for each timescale. The determination of changes in STP through a scenario-based approach offers a framework to rigorously investigate the effects of persistence at different timescales on lake cyanobacterial HABs.
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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 | 0.000 |
| 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".