{"id":"W2967927294","doi":"10.17632/k44szs77fd.1","title":"Data for: Reconstruction of a glacial lake based on LiDAR DEM from south-central Quebec (Canada): implications for the glacial lake coverage at the southern Laurentide ice sheet margin","year":2019,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glacial period; Geology; Ice sheet; Margin (machine learning); Lidar; Wisconsin glaciation; Physical geography; Oceanography; Geomorphology; Ice stream; Remote sensing; Cryosphere; Sea ice; Geography","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":[0.0001692637,0.0003730824,0.0002347006,0.001636111,0.001129225,0.0009088078,0.0008102194,0.0004251599,0.02400894],"category_scores_gemma":[0.0007531155,0.0002291534,0.000272994,0.003954064,0.0002811878,0.0003521085,0.0003399365,0.0004470923,0.003185698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01238808,"about_ca_system_score_gemma":0.01918268,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950159,"about_ca_topic_score_gemma":0.9977545,"domain_scores_codex":[0.9999065,0.000004199365,0.000004581842,0.00001772943,0.00003630931,0.00003065494],"domain_scores_gemma":[0.9993896,0.00001868875,0.0000272417,0.00002484599,0.0004758835,0.00006374687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000567082,0.0002332985,0.2443038,0.0005732984,0.000260011,0.0007063361,0.001450829,0.04963696,0.006951444,0.004207854,0.5434244,0.1476847],"study_design_scores_gemma":[0.0003572738,0.00002707641,0.6373419,0.0003679592,0.0001132884,0.0001337204,0.002188723,0.06363085,0.003772968,0.0008920001,0.2910275,0.0001467674],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2528141,0.0003822058,0.003515916,0.001135099,0.0001094863,0.000301075,0.7075542,0.001608307,0.03257968],"genre_scores_gemma":[0.623283,0.0004780601,0.01591451,0.0002654566,0.00002400578,0.0002862162,0.3279823,0.0003046353,0.03146175],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02400894,"threshold_uncertainty_score":0.08988225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002921497368801,"score_gpt":0.2149501912989441,"score_spread":0.1949209763252561,"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."}}