{"id":"W2981358566","doi":"10.4095/288669","title":"Description of watershed outline and Water Depth Survey Datasets for Cape Dorset, Nunavut","year":2011,"lang":"en","type":"report","venue":"","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Cape; Watershed; Geography; Archaeology; Cartography; Environmental science; Computer science; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005291284,0.0008100287,0.0005636383,0.003791639,0.001100704,0.001471627,0.002000239,0.0004181277,0.01715844],"category_scores_gemma":[0.001977772,0.0005506066,0.0004752981,0.0093408,0.0003473054,0.0006217011,0.000772649,0.0006176655,0.008928637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003683185,"about_ca_system_score_gemma":0.003878759,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7105443,"about_ca_topic_score_gemma":0.7772784,"domain_scores_codex":[0.9994559,0.00004015532,0.00006789823,0.0001893287,0.0001615105,0.00008514673],"domain_scores_gemma":[0.9988769,0.00008934442,0.00009197248,0.000179594,0.000650102,0.0001120793],"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":[0.0002618097,0.0002078789,0.07453916,0.0008204263,0.0001268559,0.0005417533,0.000881982,0.01938633,0.004108243,0.003430029,0.8296712,0.06602432],"study_design_scores_gemma":[0.0002268137,0.00003716598,0.2032751,0.0004403246,0.00004518243,0.0002432761,0.001678392,0.0137834,0.002408579,0.00202088,0.7756554,0.0001855321],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007400917,0.0001054663,0.001635222,0.00007704012,0.00002742505,0.0002707287,0.9850624,0.0009942951,0.004426492],"genre_scores_gemma":[0.009958245,0.00009667983,0.005797419,0.00003252141,0.000005578261,0.0005875538,0.9816057,0.0001710425,0.00174519],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2894557,"threshold_uncertainty_score":0.5823206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.187947787275823,"score_gpt":0.293876671625973,"score_spread":0.10592888435015,"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."}}