{"id":"W4213190603","doi":"10.1111/csp2.12656","title":"Identifying differences in roadless areas in Canada based on global, national, and regional road datasets","year":2022,"lang":"en","type":"article","venue":"Conservation Science and Practice","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Nature Conservancy of Canada; Wildlife Conservation Society Canada","funders":"Environment and Climate Change Canada","keywords":"Scale (ratio); Geography; Limiting; Footprint; Environmental resource management; Volunteered geographic information; Spatial analysis; Identification (biology); Cartography; Environmental science; Ecology; Remote sensing; Engineering; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00163568,0.00008525259,0.000083737,0.00009938978,0.0004496719,0.0001273277,0.0001968788,0.00001388383,0.0003104988],"category_scores_gemma":[0.001241654,0.00008793649,0.000006045796,0.001205251,0.0001781507,0.001575564,0.0001496359,0.0001504338,0.00000578033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197,"about_ca_system_score_gemma":0.0009891837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6996821,"about_ca_topic_score_gemma":0.494208,"domain_scores_codex":[0.9979909,0.0002088126,0.0002207596,0.0003760992,0.001039843,0.0001635978],"domain_scores_gemma":[0.9990407,0.000567471,0.0001511903,0.0001291002,0.00004873567,0.00006278988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001508313,0.0001261042,0.9712811,0.000005324576,0.000002265635,0.00001999948,0.0002171864,0.002941059,0.0004144221,0.0007710897,0.01752894,0.006541704],"study_design_scores_gemma":[0.0002474616,0.00002310987,0.910338,0.00001350309,0.000002549343,0.00003131486,0.001655204,0.06679904,0.000006984858,0.0001817793,0.02059991,0.0001011241],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97498,0.00003530186,0.00004386205,0.02158458,0.0001167286,0.0001508076,0.00004905946,0.000005532174,0.003034158],"genre_scores_gemma":[0.9838164,0.00001654316,0.0002466855,0.0158078,0.000005964354,0.00004834927,0.00002920225,0.000002172452,0.00002687298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2054741,"threshold_uncertainty_score":0.5150214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06891954616491731,"score_gpt":0.3134688562857572,"score_spread":0.2445493101208399,"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."}}