{"id":"W7008815180","doi":"","title":"Datasets for article 'Integrating Neighborhood Effect and Supervised Machine Learning Techniques to Model and Simulate Forest Insect Outbreaks in British Columbia, Canada'","year":2020,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Random forest; Supervised learning; Support vector machine; Outbreak","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":[],"consensus_categories":[],"category_scores_codex":[0.0005347788,0.0009798094,0.0006557839,0.001937976,0.002183365,0.001285994,0.002351081,0.001724373,0.02750237],"category_scores_gemma":[0.00331321,0.0004725136,0.000969781,0.004209421,0.0006425342,0.0005648496,0.0006715632,0.00126176,0.00935293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008170673,"about_ca_system_score_gemma":0.01133183,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9572089,"about_ca_topic_score_gemma":0.9779163,"domain_scores_codex":[0.9996871,0.00003501836,0.00001896851,0.00005438962,0.0001084823,0.00009617938],"domain_scores_gemma":[0.9978272,0.0002992977,0.00006183211,0.0003458706,0.001242837,0.0002228946],"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":[0.0002232473,0.00018196,0.006838411,0.0003091028,0.0000789039,0.0001433289,0.000107851,0.02452993,0.0005199763,0.002123157,0.9518037,0.0131402],"study_design_scores_gemma":[0.001059246,0.000080165,0.07465167,0.000388955,0.0001423976,0.0001563717,0.001087877,0.08967492,0.003368399,0.005839085,0.8233104,0.0002405518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02313579,0.0003092726,0.001298654,0.000803691,0.0002506604,0.0002013663,0.9591827,0.002442797,0.01237512],"genre_scores_gemma":[0.06570394,0.000451738,0.005202443,0.0002180897,0.00003583085,0.0003328095,0.9089808,0.0006897653,0.01838474],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04279113,"threshold_uncertainty_score":0.09200454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00996288759839388,"score_gpt":0.2378592586753336,"score_spread":0.2278963710769397,"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."}}