TANAGER: Design and Validation of an Automated Spectrogoniometer for Bidirectional Reflectance Studies of Natural Rock Surfaces
Bibliographic record
Abstract
Abstract Laboratory measurements of reflectance spectra of rocks and minerals at multiple viewing geometries are important for interpreting spacecraft data of planetary surfaces. However, efficiently acquiring such measurements is challenging, as it requires a custom goniometer that can accommodate multiple, bulky samples beneath a movable light source and detector. Most spectrogoniometric laboratory work to date has focused on mineral mixtures and particulates, yet it is also critical to characterize natural rock surfaces to understand the influence of texture and alteration. We designed the Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER) specifically to rapidly acquire spectra of natural rock surfaces across the full scattering hemisphere. TANAGER has its light source and the spectrometer's fiber optic mounted on rotating and tilting arcs, with a rotating azimuth stage and six‐position sample tray, all of which are fully motorized and integrated with a Malvern PanAnalytical ASD FieldSpec4 Hi‐Res reflectance spectrometer. Using well‐characterized color calibration targets, we have validated the accuracy and repeatability of TANAGER spectra. We also confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open‐source and available for use and modification by the larger scientific community.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".