THE POTENTIAL OF THE PROPOSED CANADIAN HERO MISSION FOR GEOSCIENCE APPLICATIONS
Bibliographic record
Abstract
With the development of the conceptual design of the Canadian Hyperspectral Environment and Resource Observer (HERO) mission, its performance needs to be evaluated for different applications areas. Accordingly, this paper investigated the potential of the HERO mission for geoscience applications, such as mineral identification for mapping, exploration and monitoring mine tailings. Hyperspectral data were simulated to match the HERO characteristics, using as input fraction maps derived from airborne data and high-resolution library spectra to represent the endmembers of the different materials. Noise and various sensor-related artifacts were added to match the expected HERO characteristics. The original data and the simulated data were processed the same way, applying a MODTRAN 4.2 based atmospheric correction prior to spectral linear unmixing to produce mineral abundance maps. The results indicate the original data and HERO simulated data products are generally similar. However, significant differences occur for the smaller fractions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".