{"id":"W2232680801","doi":"10.6084/m9.figshare.1187113.v4","title":"Lab 3: Techniques & data: field sample training with animals (pans, pitfalls, sweeps, and distances) - Distance Based Dataset","year":2014,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Sample (material); Field (mathematics); Geography; Computer science; Mathematics; Meteorology; Physics","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":[0.001196716,0.001339043,0.0008777074,0.001342412,0.0005594936,0.0007898328,0.001881075,0.001053184,0.04971146],"category_scores_gemma":[0.003902704,0.0004009589,0.0007402287,0.001528097,0.0003436263,0.0009700171,0.001331719,0.001221372,0.06796154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007537976,"about_ca_system_score_gemma":0.00111537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01114382,"about_ca_topic_score_gemma":0.0260382,"domain_scores_codex":[0.999082,0.0001159554,0.0001046555,0.0003440223,0.0002202635,0.0001331118],"domain_scores_gemma":[0.9973608,0.0003992725,0.000212397,0.0007290117,0.001089108,0.0002094941],"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":[0.0003125384,0.0003254671,0.01357161,0.0006094775,0.00004904875,0.00008649569,0.0001308165,0.001765368,0.001906324,0.0004010744,0.9422766,0.03856518],"study_design_scores_gemma":[0.0005414684,0.0003429561,0.08084597,0.0003836453,0.000072459,0.0002321252,0.0008670586,0.01300665,0.006887479,0.002862374,0.8938036,0.0001541493],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004915114,0.00004253284,0.00275086,0.000123907,0.00008018735,0.0002021242,0.9858043,0.003846689,0.002234355],"genre_scores_gemma":[0.008371164,0.00003346059,0.009631196,0.0001250166,0.00002042919,0.001118212,0.9774436,0.0003483778,0.002908443],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04971146,"threshold_uncertainty_score":0.1663014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07970254693977186,"score_gpt":0.2948278131189302,"score_spread":0.2151252661791583,"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."}}