Classification of imaging spectrometers for remote sensing applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The continuing development of new and fundamentally different classes of imaging spectrometers has increased the complexity of the field of imaging spectrometry. The rapid pace at which new terminology is introduced to describe the new types of imaging spectrometers sometimes leads to confusion, particularly in discussions of the relative merits of the different types. In some cases, multiple different terms are commonly used to describe the same fundamental approach, and it is not always clear when these terms are synonymous. Other terminology in common use is overly broad. When a single term may encompass instruments that operate in fundamentally different ways, important distinctions may be obscured. In the interest of clarifying the terminology used in imaging spectrometry, we present a comprehensive system for classification of imaging spectrometers based on two fundamental properties: the method by which they scan the object spatially, and the method by which they obtain spectral information.
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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 | 0.000 | 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) | 0.000 | 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 it