{"id":"W3216452310","doi":"10.1111/cobi.13868","title":"An introduction to decision science for conservation","year":2021,"lang":"en","type":"review","venue":"Conservation Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada; University of Victoria; University of British Columbia","funders":"","keywords":"Decision analysis; Decision engineering; Management science; Decision support system; CLARITY; Evidential reasoning approach; Computer science; Business decision mapping; Terminology; R-CAST; Decision theory; Decision tree; Data science; Knowledge management; Artificial intelligence; Engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.01155997,0.002584439,0.002037213,0.003894747,0.002340823,0.009407158,0.003612685,0.007934424,0.0341296],"category_scores_gemma":[0.02172606,0.001029475,0.002467019,0.005206248,0.0102209,0.01003368,0.004092016,0.01255231,0.008276407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007230552,"about_ca_system_score_gemma":0.007746556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003663858,"about_ca_topic_score_gemma":0.003638857,"domain_scores_codex":[0.9886042,0.005949657,0.001002714,0.001119447,0.002919985,0.0004040991],"domain_scores_gemma":[0.9634647,0.03183503,0.0009885324,0.001121624,0.001811381,0.0007787295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002325357,0.00004999616,0.0002759367,0.001826811,0.00005738695,0.0001474721,0.0004704498,0.005940386,0.0001750017,0.7972103,0.09494135,0.09888164],"study_design_scores_gemma":[0.000008831609,0.00002284166,0.0001191517,0.001539424,0.000009334419,0.0001050918,0.0001341014,0.002390313,0.00005308976,0.5835938,0.4119908,0.00003326581],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.001246338,0.2557983,0.387593,0.1022007,0.01792514,0.0006381214,0.001633417,0.0009635199,0.2320014],"genre_scores_gemma":[0.07379427,0.3562831,0.4270527,0.03824849,0.03424332,0.002871835,0.002045671,0.0009098023,0.06455076],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.0341296,"threshold_uncertainty_score":0.1141748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041299842149063,"score_gpt":0.3935006164799123,"score_spread":0.289370632265006,"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."}}