Supporting Information for "Evidence for a Locally Thinned Lithosphere Associated with Recent Volcanism at Aramaiti Corona, Venus"
Bibliographic record
Abstract
e#_output_##.dat: Data and modeled results containing the topographic profiles and axisymmetric flexure modeling corresponding to figures 3a, 4a, and 5a. Two files for each profile exist corresponding to E = 5, 65 GPa. Columns are as follows: - profile # - distance from beginning of profile (m) - detrended stereo elevation (m) - best fit he (km) - modeled deflection outboard of load (m) - radial flexural stress (Pa) ------------------------------------------------------------- fig3b_profile5.dat: Data corresponding to figure 3b, he vs RMS misfit for different combinations of r1 and r2 for profile 5. Columns are as follows: - he (km) - r1 (km) - r2 (km) - RMS misfit (m) ------------------------------------------------------------- fig_4b_all.dat: Data corresponding to figure 4b, he vs RMS misfit for all profiles. Columns are as follows: - profile # - he (km) - RMS misfit (m) ------------------------------------------------------------- swath2_narina_clip.zip: - clip of the stereo-derived topography used for flexural modeling in this study - the zipfile contains a geotiff (geolocated tiff file for use in GIS software) and auxiliary files. - References below. Full documentation of the processing applied is now available at the PDS https://pds-geosciences.wustl.edu/mgn/urn-nasa-pds-magellan_stereo_topography/document/magellan_stereo_topography_description.pdf Herrick, R. R., Stahlke, D. L., & Sharpton, V. L. (2012). Fine‐scale Venusian topography from Magellan stereo data. Eos, Transactions American Geophysical Union, 93(12), 125-126. doi:10.1029/2012EO120002 Herrick, R. R. (2020). MGN RDRS Magellan Stereo-Derived Topography Mosaic (Derived Data). NASA Planetary Data System. doi:10.17189/1519332
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.803 | 0.282 |
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".