{"id":"W3156005745","doi":"","title":"LibGuides: SANDS - Spatial and Numeric Data Services: Canadian Federal Data","year":2011,"lang":"en","type":"libguides","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Spatial analysis; Computer science; Geography; Data science; Remote sensing","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001429274,0.001527412,0.0009880809,0.007468903,0.003610106,0.004978962,0.002608299,0.0008503146,0.1324429],"category_scores_gemma":[0.00794148,0.001247362,0.0008934159,0.02085781,0.0006641235,0.002227635,0.001941395,0.001689884,0.04892729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03401426,"about_ca_system_score_gemma":0.09135217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9915999,"about_ca_topic_score_gemma":0.991716,"domain_scores_codex":[0.9982455,0.00007975953,0.0000966048,0.0001170429,0.001149851,0.000311152],"domain_scores_gemma":[0.9924097,0.000256954,0.000156948,0.0004697618,0.006284694,0.0004218587],"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":[0.00004649639,0.00001710784,0.002089769,0.000140403,0.00001527054,0.00002124476,0.0001266869,0.0007433075,0.0001019207,0.003814962,0.9650177,0.02786514],"study_design_scores_gemma":[0.00003923506,0.000003731187,0.008593279,0.0001771194,0.00002124344,0.00001871665,0.0002887368,0.002373254,0.0006955838,0.001674051,0.98606,0.00005499692],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.001708032,0.0004045574,0.001820098,0.0007010393,0.00009731347,0.0001500682,0.8879358,0.006306548,0.1008765],"genre_scores_gemma":[0.01551732,0.00150309,0.01277276,0.0003278341,0.00004706428,0.0004235046,0.7924104,0.004590116,0.1724079],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.867557,"threshold_uncertainty_score":0.4430657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02795055953530917,"score_gpt":0.2288913528400646,"score_spread":0.2009407933047554,"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."}}