End‐user satisfaction with Hurricane Dorian information in Atlantic Canada
Bibliographic record
Abstract
Abstract Both Environment and Climate Change Canada (ECCC) and the National Oceanic and Atmospheric Administration have focused significant time and resources towards improving their forecast products. However, weather prediction remains an imperfect science, and as such, it is not unusual for meteorologists to prioritize accuracy over consistency or vice versa. There is considerable debate within the literature about whether (and how) inaccuracies and/or inconsistencies in forecasting will affect end‐user trust in future warnings. Hurricane Dorian presented the opportunity to explore the intersection between these concepts as its messaging was at times both inaccurate (e.g., then‐President Donald J. Trump indicated the storm would directly affect the state of Alabama) and inconsistent (i.e., both the storm's forecasted intensity and track changed over time). Two research projects were undertaken in Atlantic Canada: the first utilized semi‐structured interviews to examine the ways that ECCC meteorologists (n = 6) perceived the needs of their end‐users during the storm. There was considerable concern that changes in the storm's forecasted track and intensity would negatively influence public response. The second project utilized a large sample questionnaire (n = 1218) to examine ways that end‐users searched for, shared, and responded to storm‐related information. Despite changes in the storm's track and intensity as it approached Atlantic Canada, as well as the international news coverage of Sharpiegate, respondents overwhelmingly agreed that the storm was well forecasted and its impacts were well predicted. The implications for this (seemingly) contradictory response are explored in the context of probabilistic forecast potential.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".