Mapping Human Health Outcomes in Relation to Harmful Algal Blooms Using a Translational Research Framework
Bibliographic record
Abstract
Abstract Aim: To evaluate Human Health Outcomes (HHO) after exposure to Harmful Algal Blooms (HABs) in aging adults through the National Institute of Environmental Science, Research Translational Framework (NIEHS-TRF). Our program of research required a cohesive framework for continued multidisciplinary collaboration (environmental sciences, epidemiology, & nursing) and research dissemination in healthcare literature. The initial research (2018) during a bloom detected cyanobacterial microcystins in the nares of 95% of aging adults (n = 125) while a different analysis (2023) indicated that residential and recreational exposures were significantly associated with increased risks of respiratory (74%), gastrointestinal (35%) and ocular symptoms (62%). A recent study (2025) affirmed that frequencies of reporting for these symptom groups were significantly higher during bloom periods than non-bloom periods. It became important to diagram our research trajectory. The process of cartography consisted of three steps on the NIEHS-TRF: 1) identification of research categories and activities (depicted visually by rings/nodes) that linked to research program outcomes, 2) within each category (visual ring), linked specific works and program outcomes to activities (visual nodes), and 3) coherently depicted visually as an overall map. The outcomes of the cartography process yielded a cohesive research trajectory, informed grant proposals, guided manuscripts and abstracts all focused-on understanding and addressing potential HHO of exposure to HABs in the aging adult population. Cartography using the NIEHS-TRF framework is recommended for nurse scientists and clinical researchers to support purposeful multidisciplinary collaboration and to strengthen planning and evaluation with clear visualization(s).
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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 | 0.065 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".