{"id":"W6944191133","doi":"10.17605/osf.io/jfy35","title":"Supplementary data and scripts for: Four steps to strengthen connectivity modeling","year":2021,"lang":"en","type":"dataset","venue":"OSF Preprints (OSF Preprints)","topic":"Wildlife-Road Interactions and Conservation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rigour; Scripting language; Raw data; Workflow; Field (mathematics); Task (project management); Sensitivity (control systems)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008619292,0.002196047,0.001670048,0.005560586,0.001764425,0.005918875,0.004957851,0.003295854,0.639362],"category_scores_gemma":[0.08393414,0.002047203,0.003543763,0.004443371,0.0008439446,0.006781801,0.005492422,0.003273138,0.2220277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002557789,"about_ca_system_score_gemma":0.007280715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01015043,"about_ca_topic_score_gemma":0.02525789,"domain_scores_codex":[0.9961665,0.001351376,0.0007297533,0.0007040926,0.000799349,0.0002489657],"domain_scores_gemma":[0.9357114,0.04560271,0.003189676,0.006018995,0.007623249,0.001854037],"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":[0.000222341,0.00005446991,0.002610274,0.003959132,0.00009104246,0.0001533748,0.0003070188,0.001180511,0.0003253653,0.004439476,0.9659385,0.02071844],"study_design_scores_gemma":[0.0007373486,0.00006631626,0.005489767,0.003954125,0.0001552157,0.0003004047,0.0004305035,0.004530362,0.001071759,0.03129205,0.9517976,0.0001746005],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009086967,0.0002656557,0.02144314,0.001685705,0.0004306835,0.0008019319,0.939486,0.02391297,0.01106539],"genre_scores_gemma":[0.01502888,0.0007914546,0.1877649,0.005808242,0.0004008329,0.01350534,0.7147038,0.03883196,0.0231646],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.639362,"threshold_uncertainty_score":0.5144063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05247967383204918,"score_gpt":0.2866697415978665,"score_spread":0.2341900677658173,"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."}}