{"id":"W6931943878","doi":"10.5683/sp3/xkoz67","title":"Northern Whaling and Trading Company Shed -- Herschel Island -- Laser Scanning -- Metadata -- 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":"Whaling; Metadata; Data set; Set (abstract data type); Laser scanning","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":[0.0009169055,0.002004914,0.00115328,0.002133468,0.0009024785,0.001664902,0.002237237,0.001449396,0.01902498],"category_scores_gemma":[0.00229169,0.0005461523,0.0009242989,0.003585725,0.0006324849,0.0009382876,0.00182343,0.001149578,0.04555197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255905,"about_ca_system_score_gemma":0.001860514,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06640577,"about_ca_topic_score_gemma":0.1816926,"domain_scores_codex":[0.999061,0.0001002046,0.00007156859,0.000288208,0.0003111141,0.0001678496],"domain_scores_gemma":[0.999043,0.0001334205,0.00009687156,0.0003039043,0.0003065128,0.0001162217],"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":[0.00013359,0.00005012144,0.005196685,0.0005046112,0.00005367941,0.00008213697,0.0001085797,0.0005770373,0.0007231284,0.0004608954,0.9835752,0.008534216],"study_design_scores_gemma":[0.00009229251,0.0000301239,0.02800474,0.000206141,0.00003762113,0.0001606342,0.0003130107,0.0007746742,0.001325987,0.0008021014,0.9681837,0.00006896429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001334387,0.0001079194,0.000195475,0.00005844151,0.00005937564,0.00001799657,0.9958412,0.0006890845,0.001696001],"genre_scores_gemma":[0.001039788,0.00003206051,0.0004114726,0.00002064974,0.00000716358,0.00003159252,0.9976718,0.00006053943,0.0007249257],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9335942,"threshold_uncertainty_score":0.1320385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667020554878936,"score_gpt":0.2718896840770718,"score_spread":0.2452194785282825,"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."}}