{"id":"W3011017609","doi":"10.11588/data/ou8ya1","title":"3D Micro-Mapping of Subsidence Stations [Source Code and Data]","year":2019,"lang":"en","type":"dataset","venue":"University Library Heidelberg","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tundra; Permafrost; Point cloud; Subsidence; Geology; Remote sensing; Orientation (vector space); Source code; Data source; Geodesy; Displacement (psychology); Geomorphology; Arctic; Computer science; Database; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000614851,0.001945689,0.0009491525,0.002315285,0.0007334984,0.001506751,0.002214489,0.001463755,0.05442385],"category_scores_gemma":[0.002431896,0.0006623416,0.001011394,0.004521623,0.0004626439,0.001226802,0.00201605,0.001642527,0.07593466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001154818,"about_ca_system_score_gemma":0.001768724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03938001,"about_ca_topic_score_gemma":0.08427695,"domain_scores_codex":[0.9993799,0.00006088298,0.00006295763,0.000186141,0.000182656,0.0001274921],"domain_scores_gemma":[0.9989671,0.0001671156,0.00008977074,0.0002873968,0.0003744199,0.0001142365],"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":[0.00005212387,0.00003445805,0.00163164,0.0004371214,0.00002843311,0.00004242174,0.00005853189,0.0008393583,0.0003866924,0.0006609816,0.9911308,0.004697396],"study_design_scores_gemma":[0.0001467987,0.00001259871,0.007635855,0.0002225463,0.00002153013,0.0000785748,0.0001275069,0.00135704,0.0009480697,0.001889362,0.9875097,0.00005059883],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002412688,0.00002437647,0.0002660168,0.00003121728,0.00001640345,0.00001398491,0.9978448,0.0008199721,0.0007419868],"genre_scores_gemma":[0.0006286389,0.00002663476,0.001022243,0.00002131106,0.000003360737,0.00007038807,0.9975317,0.0002255187,0.0004702365],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05442385,"threshold_uncertainty_score":0.1820659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05553004345785997,"score_gpt":0.2268790668036116,"score_spread":0.1713490233457516,"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."}}