Geographic Variability, Seasonality, and Increase in ASPCA Animal Poison Control Center Harmful Blue-Green Algae Calls—United States and Canada, 2010–2022
Bibliographic record
Abstract
Harmful cyanobacteria (blue-green algae) exposures can cause illness or death in humans and animals. We characterized American Society for the Prevention of Cruelty to Animals (ASPCA) Animal Poison Control Center (APCC) harmful blue-green algae (HBGA) call data, compared it to a measure of harmful algal bloom public awareness, and considered its suitability as a public health information source. ASPCA APCC dog and cat “HBGA exposure” calls made 1 January 2010–31 December 2022 were included. We calculated annual HBGA call percentages and described calls (species, month, origin, exposure route). We characterized public awareness by quantifying Nexis Uni® (LexisNexis Academic; New York, NY, USA)-indexed news publications (2010–2022) pertaining to “harmful algal bloom(s)”. Call percentage increased annually, from 0.005% (2010) to 0.070% (2022). Of 999 HBGA calls, 99.4% (n = 993) were dog exposures. Over 65% (n = 655) of calls were made July–September, largely from the New England (n = 154 (15.4%)) and Pacific (n = 129 (12.9.%)) geographic divisions. Oral and dermal exposures predominated (n = 956 (95.7%)). Harmful algal bloom news publications increased overall, peaking in 2019 (n = 1834). Higher call volumes in summer and in the New England and Pacific geographic divisions drove HBGA call increases; public awareness might have contributed. Dogs and humans have similar exposure routes. ASPCA APCC HBGA call data could serve as a public health information source.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".