{"id":"W6999066016","doi":"","title":"Canada VMap1, Library 18: Vegetation Text Features","year":2016,"lang":"en","type":"other","venue":"The Faculty Digital Archive (New York University)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scale (ratio); Product (mathematics); Topographic map (neuroanatomy); Vector map; Geographic information system; Vegetation (pathology); Natural resource; Digital mapping; National library","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":[0.0005161719,0.001137969,0.0007208405,0.0083281,0.003724899,0.004987568,0.002316124,0.0005011253,0.4541342],"category_scores_gemma":[0.004332249,0.0006709795,0.0005159456,0.02511494,0.0005454139,0.002061921,0.001538082,0.0006912065,0.2512529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01288758,"about_ca_system_score_gemma":0.04213775,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.913983,"about_ca_topic_score_gemma":0.9321826,"domain_scores_codex":[0.9990575,0.00002828676,0.00003791777,0.0001154472,0.0005928351,0.0001680505],"domain_scores_gemma":[0.9954675,0.0001670076,0.0001196241,0.0002604057,0.003571194,0.000414148],"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":[0.0000203254,0.000007428043,0.0003701206,0.0001146506,0.000002084284,0.00002408964,0.0001108493,0.00008201088,0.0001095913,0.00100914,0.9741068,0.02404275],"study_design_scores_gemma":[0.000005625423,0.000002131483,0.002369248,0.00004256264,0.000002780188,0.0000206088,0.0001253832,0.0001092768,0.0002100969,0.0002569888,0.9968421,0.00001316318],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001266422,0.0002834922,0.001335701,0.0003677089,0.0001749985,0.0002031169,0.6702545,0.005886603,0.3202276],"genre_scores_gemma":[0.009706551,0.0009645334,0.006337324,0.0002146184,0.00009531176,0.0002783043,0.4486113,0.006359628,0.5274324],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4541342,"threshold_uncertainty_score":0.7786113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618725293975229,"score_gpt":0.2145071720714099,"score_spread":0.1983199191316576,"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."}}