{"id":"W4409260606","doi":"10.1088/2631-8695/adca88","title":"Real-time point localization on plants using feature-based soft margin SVM-PCA method","year":2025,"lang":"en","type":"article","venue":"Engineering Research Express","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Margin (machine learning); Pattern recognition (psychology); Support vector machine; Feature (linguistics); Artificial intelligence; Computer science; Point (geometry); Principal component analysis; Biological system; Mathematics; Machine learning; Biology","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":[0.0006312185,0.000882154,0.0007674317,0.0008635832,0.0002836999,0.0006838793,0.000972372,0.0009994122,0.001724219],"category_scores_gemma":[0.001115419,0.000284788,0.0007602626,0.0006846227,0.0004106819,0.0007130689,0.0006257438,0.001048021,0.0009553792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004867407,"about_ca_system_score_gemma":0.000664314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00331886,"about_ca_topic_score_gemma":0.002365732,"domain_scores_codex":[0.9995284,0.0000717244,0.00002347673,0.0001712512,0.0001453477,0.00005975222],"domain_scores_gemma":[0.9993916,0.000153895,0.00007710812,0.00007809715,0.0002616569,0.00003776679],"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":[0.0003822651,0.0002048969,0.002240749,0.0001263323,0.00007762411,0.00009212713,0.0000716565,0.2782508,0.0414483,0.001642728,0.003565185,0.6718974],"study_design_scores_gemma":[0.000003369194,0.00002434671,0.0004017131,0.000002653206,0.000003952104,0.00001221307,0.000004730609,0.9963245,0.00279744,0.0002405979,0.0001805912,0.000003828173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03557722,0.000167845,0.9616859,0.00009976963,0.0000519959,0.00003870627,0.00006086303,0.001559165,0.0007585403],"genre_scores_gemma":[0.7384186,0.0002007084,0.256865,0.0001139704,0.00006627693,0.0001005926,0.0003790123,0.0001187612,0.003736973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00331886,"threshold_uncertainty_score":0.006599069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03125735040487745,"score_gpt":0.3070635399482219,"score_spread":0.2758061895433445,"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."}}