{"id":"W2955421088","doi":"10.5565/rev/elcvia.1041","title":"Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models","year":2019,"lang":"en","type":"article","venue":"ELCVIA Electronic Letters on Computer Vision and Image Analysis","topic":"Date Palm Research Studies","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Outlier; Artificial intelligence; Pattern recognition (psychology); Mixture model; Computer science; Benchmark (surveying); Anomaly detection; Gaussian; Variation (astronomy); Pruning; Mathematics","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":[0.000932556,0.001248348,0.001747943,0.004233617,0.0004407029,0.001165647,0.001750959,0.001161159,0.0007998599],"category_scores_gemma":[0.00201917,0.0003813946,0.00159305,0.002274096,0.0004392976,0.001431586,0.000948874,0.001056991,0.001264282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003987491,"about_ca_system_score_gemma":0.0004165183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001736848,"about_ca_topic_score_gemma":0.002117583,"domain_scores_codex":[0.9985573,0.0001570642,0.00009261373,0.0004933674,0.0005648755,0.0001348625],"domain_scores_gemma":[0.999051,0.0002073988,0.0001664412,0.0001729847,0.000353583,0.00004845407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00047971,0.0001703856,0.01083109,0.000248247,0.0001983657,0.0004563101,0.0001398688,0.026073,0.09118477,0.002151996,0.00398923,0.8640771],"study_design_scores_gemma":[0.00002923272,0.0002810326,0.02102287,0.00003865619,0.0001396436,0.001609391,0.0002053726,0.876923,0.08367406,0.003883479,0.01204489,0.000148342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03716647,0.0007522801,0.9570187,0.00006837768,0.00009906875,0.00006223685,0.000215948,0.003826083,0.0007908097],"genre_scores_gemma":[0.3566864,0.001039473,0.6363387,0.0001125117,0.0001250986,0.0001341401,0.001702577,0.000359005,0.003502056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004233617,"threshold_uncertainty_score":0.004931927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148478501245503,"score_gpt":0.2471885183794819,"score_spread":0.2357037333670269,"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."}}