{"id":"W6958811870","doi":"10.6084/m9.figshare.24425803","title":"Additional file 2 of Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models","year":2024,"lang":"en","type":"other","venue":"Figshare","topic":"Synthesis and Properties of Aromatic Compounds","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Random forest; Climate change; Oncomelania hupensis; Distribution (mathematics); Artificial neural network","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002955204,0.0001441014,0.0002759859,0.00007752602,0.00001694309,0.00001416675,0.00007576881,0.0002278848,0.979748],"category_scores_gemma":[0.0003845361,0.0001281711,0.00006625412,0.00009970873,0.000004394198,0.00005747019,0.00005890847,0.0001894327,0.0001463305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001756457,"about_ca_system_score_gemma":0.00003533371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001359249,"about_ca_topic_score_gemma":0.0000798454,"domain_scores_codex":[0.9991816,0.00001921434,0.0002309094,0.0001706639,0.0002717357,0.0001258421],"domain_scores_gemma":[0.9990879,0.0002470199,0.0004744709,0.000117361,0.00005418381,0.00001901948],"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":[0.00001023933,0.00002426069,0.00004611588,0.001646541,0.00006012239,0.000002033169,0.00004243457,0.00002201599,0.0000222249,5.666645e-7,0.9977544,0.0003690544],"study_design_scores_gemma":[0.0001448749,0.0000314668,0.0001359307,0.06465753,0.00005006684,0.000004067498,0.0000387991,0.03391897,0.0001854425,0.00001798305,0.9006357,0.0001791872],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001941155,0.0002540064,1.697594e-7,0.000003776829,0.000010637,0.00006518079,0.9045172,0.00005479289,0.09507481],"genre_scores_gemma":[0.001333918,0.00001191894,0.0001351622,0.00000202753,0.0001515992,0.0002366141,0.9789155,0.0001214055,0.01909188],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9796016,"threshold_uncertainty_score":0.5226665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0539235965779001,"score_gpt":0.2213658258929063,"score_spread":0.1674422293150062,"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."}}