Integrating Earth observation science into environmental impact assessment
Bibliographic record
Abstract
Environmental Impact Assessment (EIA) processes have well-established requirements for baseline data describing the status and trends of rapidly changing environments. Examination of these requirements demonstrates a strong capacity for Earth Observation (EO) science to support EIA processes. This capacity has not been matched with EO uptake indicating substantial persistent barriers, including: (1)-EO science awareness; (2)-data availability and usability; and (3)-technological solutions and analytics capacity. The Canada Centre for Mapping and Earth Observation is at the mid-point of a ten-year Earth Observation for Cumulative Effects (EO4CE) research program to integrate EO within Canada’s EIA processes. The EO4CE program has employed a wide variety of data systems (optical, microwave, gravity, etc.) and methods (machine learning, big data systems, etc.) to build biosphere, hydrosphere, and cryosphere data products with national scale coverage and regional scale detail. Next steps for the program include preparing for inclusion of next-gen sensors, improving data production frameworks, and addressing awareness and uptake issues through focused communication and demonstration. This presentation will provide an overview of EO4CE implementation, results, and lessons learned which would be applicable to similar initiatives.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".