{"id":"W4393652799","doi":"10.5281/zenodo.6465111","title":"Supplementary Data - \"Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio\"","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Ridge; Geology; Wrinkle; Geomorphology; Paleontology; Materials science","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":[0.001272667,0.001585471,0.001014845,0.002974601,0.000726752,0.002169601,0.002369246,0.001662126,0.1556995],"category_scores_gemma":[0.005411543,0.0005729585,0.001108634,0.004905457,0.0004506714,0.001260192,0.001674774,0.001285188,0.1100706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001441049,"about_ca_system_score_gemma":0.002135891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03082915,"about_ca_topic_score_gemma":0.07414604,"domain_scores_codex":[0.9991419,0.0001399812,0.0001255488,0.0002288033,0.0002445164,0.0001192277],"domain_scores_gemma":[0.9973525,0.0007631765,0.0002207942,0.0005228903,0.0009813894,0.0001593035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005581516,0.00003573764,0.002295198,0.0007899746,0.00003735571,0.00004906926,0.00005158781,0.0006480992,0.0001919216,0.0004725161,0.9920985,0.003274199],"study_design_scores_gemma":[0.0001575769,0.00001586958,0.01231372,0.000466774,0.0000363389,0.000121038,0.0002669875,0.0006542483,0.0004999468,0.001520567,0.9838987,0.00004830425],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001601424,0.00002505417,0.0001027453,0.0000314518,0.00001827086,0.000008945965,0.9990275,0.0001836256,0.0004422484],"genre_scores_gemma":[0.0006167064,0.00002838255,0.0005464646,0.00002932308,0.000005541049,0.00007911118,0.99791,0.00008362893,0.0007007706],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1556995,"threshold_uncertainty_score":0.5208667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09018028084620246,"score_gpt":0.2902396740486419,"score_spread":0.2000593932024395,"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."}}