{"id":"W6913023584","doi":"10.5683/sp2/qewj8y","title":"Small House No.12 -- Herschel Island -- Laser Scanning -- 2019","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Laser scanning; Data set; Laser; Window (computing)","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":[0.0005585271,0.001799999,0.001207347,0.001850314,0.000769356,0.001453068,0.002021928,0.0008881878,0.03699484],"category_scores_gemma":[0.001292883,0.0005635496,0.0006328431,0.003346463,0.0005278966,0.0008890552,0.001133529,0.0009589937,0.0725109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004589,"about_ca_system_score_gemma":0.00143043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06415789,"about_ca_topic_score_gemma":0.2035916,"domain_scores_codex":[0.9993112,0.00005360432,0.00003309071,0.0002370481,0.0002442315,0.0001208327],"domain_scores_gemma":[0.9992112,0.00005702968,0.00006712609,0.0002508042,0.0003090826,0.000104686],"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":[0.00009648932,0.0000255642,0.002457701,0.0002587046,0.00003746798,0.00004873727,0.00006508211,0.0003513767,0.0006957913,0.0002991425,0.9885272,0.007136871],"study_design_scores_gemma":[0.00006993725,0.0000227958,0.02097559,0.0001042674,0.0000283393,0.0001161943,0.0001856578,0.0006518847,0.001633755,0.0007585885,0.975395,0.00005784261],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001132845,0.00008456253,0.000311786,0.00004398215,0.00004295937,0.00001989381,0.9940631,0.001702061,0.0025988],"genre_scores_gemma":[0.001593622,0.00002794569,0.0006794877,0.00001579409,0.00000788121,0.00003349967,0.9962562,0.0002447526,0.001140883],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9358421,"threshold_uncertainty_score":0.1275689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746287085750932,"score_gpt":0.2583794818445498,"score_spread":0.2309166109870405,"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."}}