{"id":"W2620845443","doi":"","title":"Investigating Martian and Venusian hyperspectral datasets","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"","keywords":"Hyperspectral imaging; Martian; Venus; Astrobiology; Remote sensing; Computer science; Mars Exploration Program; Environmental science; Artificial intelligence; Geology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008998219,0.0008613819,0.0004164744,0.001890713,0.0009893327,0.001672339,0.000721559,0.001036521,0.001772226],"category_scores_gemma":[0.001607266,0.0002694992,0.0006950453,0.001898638,0.0003670755,0.001090489,0.0005098138,0.0005769597,0.0004232438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005705775,"about_ca_system_score_gemma":0.0007703839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03436131,"about_ca_topic_score_gemma":0.084302,"domain_scores_codex":[0.9995794,0.00004569603,0.00001077447,0.0001097314,0.0001738257,0.00008065264],"domain_scores_gemma":[0.9994348,0.000142139,0.00003527845,0.000100555,0.0002227966,0.00006441534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001916651,0.001989322,0.07670079,0.0007274108,0.001110272,0.00232239,0.0009809651,0.1791585,0.2888148,0.004630184,0.07323819,0.3684106],"study_design_scores_gemma":[0.0002274162,0.0002546873,0.19858,0.00006092195,0.0003564851,0.0008461971,0.002667279,0.6861429,0.06832889,0.005163902,0.03729623,0.00007514059],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788832,0.0005800171,0.008045498,0.0006661097,0.0001328276,0.0000534838,0.003385855,0.001375257,0.006877795],"genre_scores_gemma":[0.9503837,0.0004728861,0.02420287,0.0002049648,0.0001089718,0.00002063027,0.02021794,0.0003859448,0.00400217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03436131,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509328013915672,"score_gpt":0.2229973754990666,"score_spread":0.2079040953599098,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}