{"id":"W3026842959","doi":"10.1016/j.neucom.2021.10.015","title":"Two-view fine-grained classification of plant species","year":2021,"lang":"en","type":"preprint","venue":"Neurocomputing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Scalability; Convolutional neural network; Plant species; Artificial intelligence; Metric (unit); Representation (politics); Pattern recognition (psychology); Machine learning; Deep learning; Taxonomy (biology); Plant identification; Ecology; Biology; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0001565374,0.0002285134,0.0003890024,0.00001257861,0.0001165532,0.0001086627,0.0003674704,0.0001455654,0.0001084657],"category_scores_gemma":[0.00005850316,0.00008515899,0.0002282241,0.0002574047,0.00003291025,0.00003445656,0.0005199419,0.0003792098,0.000006698427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001405426,"about_ca_system_score_gemma":0.00001606016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009519433,"about_ca_topic_score_gemma":0.0003005684,"domain_scores_codex":[0.9984794,0.0001202973,0.0004203317,0.0004977845,0.0002539872,0.0002282507],"domain_scores_gemma":[0.9991006,0.000250466,0.0003566932,0.000101999,0.0001312589,0.00005893215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001004528,0.0001614976,0.007510571,0.0001634616,0.00003752701,0.00002583518,0.0002772119,0.0002760848,0.9133973,0.0005691267,0.003127927,0.07444345],"study_design_scores_gemma":[0.0002117648,0.0001312825,0.9226263,0.0007559297,0.00006980643,0.00003387346,0.0007427623,0.001755608,0.01669263,0.0002129071,0.05614029,0.0006268912],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953076,0.0004269668,0.00001211947,0.001293039,0.0005422961,0.00026545,0.00005949603,0.0000916781,0.002001309],"genre_scores_gemma":[0.9972612,0.00006856221,0.0003003872,0.0001805377,0.001272396,0.000009846906,0.0007843382,0.000001393211,0.0001212969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9151157,"threshold_uncertainty_score":0.3472683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04724023740356424,"score_gpt":0.2352160217677407,"score_spread":0.1879757843641765,"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."}}