Time and Tide: Mapping Australia's dynamic coastal zone through time and space using DEA
Bibliographic record
Abstract
Australia has a vast and highly dynamic coastline of over 30,000 kilometres with many unique environments: sandy beaches, rocky cliffs, muddy tidal flats, and mangroves. Until recently, this scale and complexity has meant that many of Australia's coastal environments have been poorly and inconsistently mapped, particularly in dynamic or remote regions where accurate survey data can be extremely challenging and costly to obtain. In recent years, however, satellites orbiting our planet have provided a new and powerful source of information about Australia's coast and how it has changed over recent decades. Digital Earth Australia is a government platform that prepares these vast volumes of satellite data and makes it available to governments and industry for easy use. This talk will showcase how new and innovative analysis techniques can be applied to petabytes of DEA satellite data to better understand and monitor Australia's vast coastal zone from space: from using the rise and fall of the tide to map the 3D shape of Australia's coast, to track how our coastline has shifted and changed over the past three decades in unprecedented scale and detail. We will demonstrate how these freely available coastal products and tools developed by Digital Earth Australia can be used by scientists, managers, policymakers and the general public to provide new information to help maintain and protect Australia's iconic shores for future generations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".