{"id":"W4408039585","doi":"10.1016/j.atech.2025.100860","title":"Accurate estimation of tuber size in large potato throughput at potato storage using machine vision and machine learning techniques","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Throughput; Computer science; Artificial intelligence; Machine learning; Agricultural engineering; Computer vision; Engineering; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001245232,0.0002551425,0.0004646126,0.0003324039,0.0001641965,0.00002114048,0.0001915144,0.0003661706,0.0001490512],"category_scores_gemma":[0.0003231908,0.0001813517,0.00006905839,0.001542033,0.0001191335,0.0001582067,0.0003531062,0.0004774703,0.000002749686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002145564,"about_ca_system_score_gemma":0.00001619289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002396627,"about_ca_topic_score_gemma":0.0002512591,"domain_scores_codex":[0.9987382,0.00002547215,0.0004059348,0.0003804311,0.0001287014,0.000321276],"domain_scores_gemma":[0.9993722,0.0001218528,0.000203946,0.0001997074,0.00007230003,0.00002998518],"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":[0.00004939535,0.000126061,0.1191249,0.0001830414,0.00008628627,0.0000147002,0.00009325243,0.0001574934,0.8777889,0.0007279067,0.00006421964,0.001583898],"study_design_scores_gemma":[0.0006316514,0.00006353808,0.01440592,0.0001441207,0.0001208725,0.00005947709,0.0004564184,0.007935105,0.9747362,0.0006326225,0.0005433999,0.000270694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948877,0.002228706,0.0007539928,0.000632955,0.00002566103,0.0001169805,0.00001884669,0.0002998077,0.001035423],"genre_scores_gemma":[0.9953119,0.0002002323,0.003284081,0.00002784859,0.00001134934,0.00001574324,0.00005643824,0.00001154816,0.00108082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1047189,"threshold_uncertainty_score":0.7395308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008537113858245132,"score_gpt":0.283472316288894,"score_spread":0.2749352024306488,"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."}}