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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".