The Coastie Initiative: Crowdsourcing Coastal Monitoring
Bibliographic record
Abstract
Coastline change is an ever-increasing area of interest with a multitude of research opportunities. The Coastie Initiative was developed with Parks Canada to crowdsource the collection of regular imagery of coastal morphology, building a large dataset for tracking shoreline change. Citizens can partake in this growing program by submitting a Coastie through the web-based platform. At every site, a phone cradle, designed to standardize images, is accompanied by an informational panel and a QR code. Visitors can use the QR code to quickly access the submission wizard. After submission, the image is saved and awaits classification by researchers. The intended development of the project includes an achievement system to promote user retention, an automated classification system to eliminate manual labelling, and an ocean literacy stream as an effort to educate and inform the public. The initiative was launched during the fall of 2021 with 5 locations, now boasting 32 sites across Canada. The Coastie program is a collaboration that has developed from the global CoastSnap Community Beach Monitoring movement that began in Australia in 2017. Using the early prototype dataset, coastal change can be quantified from repeat geo-rectified images. These capture deposition/erosion of the beach and dune topography in response to ambient environmental conditions or higher energy storm events, most notably hurricane Fiona. This dataset serves as a baseline to monitor how coastal systems respond to climate change, including sea level rise and change in storm activity. The Coastie Initiative allows citizen scientists to influence coastline exploration participating alongside motivated researchers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".