{"id":"W4394261484","doi":"10.6084/m9.figshare.4083372","title":"YorkU.PondAndGravel.Oct26-2016.csv - Census 3: Investigating the effects of ground permeability on plants and animals","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Permeability (electromagnetism); Geography; Environmental science; Chemistry; Sociology; Demography; Population; Biochemistry","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":[0.0007789766,0.001604072,0.0008681295,0.002656658,0.001767677,0.003194581,0.001449247,0.001059308,0.9333628],"category_scores_gemma":[0.002383177,0.001037795,0.0006874026,0.003193111,0.0003516918,0.002540335,0.004056443,0.0008658242,0.9205315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001597715,"about_ca_system_score_gemma":0.001756223,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06354165,"about_ca_topic_score_gemma":0.1064116,"domain_scores_codex":[0.9995135,0.00005307535,0.00003877998,0.0001103298,0.0001878889,0.00009650113],"domain_scores_gemma":[0.9979074,0.0002021226,0.00008427788,0.000496869,0.0007950287,0.000514346],"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":[0.00005313177,0.00002545981,0.0004334641,0.00006806613,0.000002240428,0.00001490984,0.00003384438,0.00005076013,0.0001478205,0.0004320841,0.9614924,0.03724571],"study_design_scores_gemma":[0.00001789299,0.000007801564,0.002200747,0.00008539298,0.000001155722,0.00001518876,0.00007048265,0.000165126,0.0002120152,0.0002801675,0.9969305,0.00001361548],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"dataset","genre_scores_codex":[0.001131757,0.0001820136,0.001508321,0.0008350614,0.0007377414,0.0002287342,0.3892252,0.01734005,0.5888111],"genre_scores_gemma":[0.003551213,0.000278302,0.001652077,0.0003378236,0.0001209491,0.0002239756,0.1823182,0.008661929,0.8028555],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.9364583,"threshold_uncertainty_score":0.1263436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322372853613971,"score_gpt":0.2385077323962169,"score_spread":0.2152840038600772,"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."}}