{"id":"W4386966525","doi":"10.1111/csp2.13024","title":"Assessing diverse evidence to improve conservation decision‐making","year":2023,"lang":"en","type":"article","venue":"Conservation Science and Practice","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada","funders":"","keywords":"Underpinning; Relevance (law); Management science; Quality (philosophy); Computer science; Risk analysis (engineering); Reliability (semiconductor); Process management; Business; Engineering; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005655506,0.0001356353,0.0001122775,0.0002472962,0.001471004,0.000821632,0.0004237503,0.00004770893,0.0002228773],"category_scores_gemma":[0.01539286,0.0001301949,0.00002402575,0.00339617,0.0005306139,0.005476579,0.0009807205,0.0001050766,0.0009519404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002264846,"about_ca_system_score_gemma":0.0001274934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007319998,"about_ca_topic_score_gemma":0.0001270372,"domain_scores_codex":[0.9974751,0.0001275969,0.0002663329,0.0006275859,0.001163466,0.0003399249],"domain_scores_gemma":[0.9965172,0.002488437,0.0002051201,0.0003486225,0.0002769122,0.0001637146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001611399,0.00005086269,0.7273946,0.00002516151,0.000011659,0.00003023039,0.0039722,0.0005632259,0.02497952,0.0002064474,0.0308022,0.2118027],"study_design_scores_gemma":[0.0001621821,0.00004610118,0.853175,0.0000783707,0.00003007821,0.000008216367,0.005280597,0.009968651,0.0001299931,0.0002539592,0.130669,0.0001978471],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9541243,0.00001987616,0.004713333,0.03519239,0.0003876347,0.0004266916,0.000002553511,0.0001263274,0.005006944],"genre_scores_gemma":[0.9627042,0.00009589767,0.01085879,0.02567364,0.00004397258,0.00002145591,0.000002623074,0.000007779368,0.000591622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2116049,"threshold_uncertainty_score":0.9998289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114518938162785,"score_gpt":0.374787057068403,"score_spread":0.260268118905618,"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."}}