{"id":"W6893446705","doi":"10.5281/zenodo.2553996","title":"Code and data from: Optimality in prioritizing conservation projects","year":2019,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Northern British Columbia","funders":"","keywords":"Ranking (information retrieval); Heuristic; Class (philosophy); Code (set theory); Heuristics; Threatened species; Evolutionary algorithm; Safeguard","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":[0.00343545,0.001526201,0.0008051076,0.001606356,0.0009311896,0.003606864,0.003300991,0.002238183,0.1365886],"category_scores_gemma":[0.04171168,0.00127544,0.001429286,0.002438372,0.001202814,0.003018533,0.002710317,0.002500197,0.06217913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615213,"about_ca_system_score_gemma":0.005444757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167932,"about_ca_topic_score_gemma":0.01162473,"domain_scores_codex":[0.9973776,0.0006061484,0.0003681598,0.0004625353,0.0009373467,0.0002482139],"domain_scores_gemma":[0.9796809,0.01278144,0.0008383886,0.002649594,0.003551237,0.0004983658],"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":[0.001059965,0.0002420833,0.007066657,0.001536828,0.00008112891,0.0003751509,0.0004696212,0.06742162,0.001338703,0.01851553,0.8254228,0.07646988],"study_design_scores_gemma":[0.001808924,0.0002041652,0.007132911,0.0008948257,0.00005637618,0.0002925529,0.0002357214,0.332358,0.007854347,0.05205937,0.596888,0.0002147978],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.02319073,0.0003996778,0.1989518,0.005574887,0.001262834,0.002377058,0.3533213,0.2964872,0.1184346],"genre_scores_gemma":[0.1340199,0.0007848101,0.332538,0.002523286,0.0002789412,0.005670653,0.3994667,0.08462287,0.04009474],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1365886,"threshold_uncertainty_score":0.4569345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.127644773991533,"score_gpt":0.3122760802577096,"score_spread":0.1846313062661766,"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."}}