{"id":"W3120672047","doi":"10.1007/s40998-020-00398-2","title":"Automated Plant Species Identification Using Leaf Shape-Based Classification Techniques: A Case Study on Iranian Maples","year":2021,"lang":"en","type":"article","venue":"Iranian Journal of Science and Technology Transactions of Electrical Engineering","topic":"Botanical Research and Applications","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Preprocessor; Petiole (insect anatomy); Support vector machine; Plant taxonomy; Feature extraction; Plant identification; Pattern recognition (psychology); Artificial intelligence; Identification (biology); Classifier (UML); Computer science; Botany; Biology; Taxonomy (biology); Genus","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":[0.0005396936,0.0001035098,0.0001942631,0.0002674922,0.0003247318,0.00006317492,0.0002750639,0.00009689466,0.00001647712],"category_scores_gemma":[0.0002803582,0.00005639348,0.00005255769,0.003274627,0.000308389,0.0001840668,0.000008986321,0.0003403581,5.903223e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008247105,"about_ca_system_score_gemma":0.0001243837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001305514,"about_ca_topic_score_gemma":0.00002142996,"domain_scores_codex":[0.9986995,0.00002608291,0.0004163847,0.0002267852,0.0003924966,0.0002387527],"domain_scores_gemma":[0.9989344,0.0001660009,0.0001469447,0.00009346826,0.0005414161,0.0001177271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001188821,0.0003019575,0.0002524889,0.000005226495,0.00001172582,0.0001339383,0.00002578969,0.0001051973,0.9636093,0.0002502849,0.000002474013,0.03528972],"study_design_scores_gemma":[0.0006045058,0.002574635,0.0269537,0.0001636625,0.0001068436,0.004913245,0.004260176,0.2420494,0.7173997,0.0001852996,0.0004266977,0.0003622241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914685,0.0000783112,0.006788203,0.001352866,0.00002546013,0.0001618864,0.00001360703,0.0001025372,0.000008645961],"genre_scores_gemma":[0.9983816,0.00003039977,0.001540852,0.000011609,0.0000208062,0.000008148418,8.363207e-7,0.000001637065,0.00000409121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2462097,"threshold_uncertainty_score":0.2497608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04006436519391227,"score_gpt":0.2766582983840029,"score_spread":0.2365939331900906,"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."}}