COASTIE Citizen Science Program for Measuring Impacts of Hurricane Fiona
Bibliographic record
Abstract
Canada's coastal systems are increasingly vulnerable to erosion due to eustatic sea level rise and changes in storm frequency and magnitude. Erosion of beach and dune systems threatens critical ecosystems and anthropogenic services, and there is a need to improve our understanding on the evolution of these systems in response to ambient environmental conditions and climate change. The COASTIE citizen science project at Canadian National Parks was established in 2021 and was designed to enable high temporal resolution monitoring of shoreline change and dune erosion and recovery through multiple seasons and storm events. Images taken by park visitors are uploaded directly to the University of Windsor's Coastal Research Group's database for image processing, rectification, and shoreline mapping and analysis through Matlab and ArcMap programs. This study focuses on an extreme erosion event associated with post-tropical storm Fiona at Cavendish and Brackley Beaches, located on the north shore of Prince Edward Island (PEI). Preliminary results demonstrate how the monitoring of coastal systems from pre and post storm events such as Fiona, allow for accurate measurements to be taken for sediment loss, shoreline retreat, and erosion of dunes and ultimately the recovery of the beach and dune. The photographic monitoring of coastal sites by citizen scientists has proven to be a powerful tool that will enhance public engagement and our understanding of coastal system dynamics through time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".