Onboard SVM analysis of Hyperion data to detect sulfur deposits in Arctic regions
Bibliographic record
Abstract
Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 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".