{"id":"W4224236327","doi":"10.3390/min12040455","title":"Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition","year":2022,"lang":"en","type":"article","venue":"Minerals","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Deep learning; Computer science; Artificial intelligence; Segmentation; Automation; Task (project management); Process (computing); Pattern recognition (psychology); Machine learning; Residual neural network; Engineering; Systems engineering","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":[0.0002024415,0.0001187151,0.0001185482,0.0001312703,0.0001933888,0.00005048664,0.0000495085,0.00002597331,0.0004716693],"category_scores_gemma":[0.00002701303,0.0001285311,0.00002833958,0.0001799575,0.00001419144,0.00009552453,0.00001978825,0.0001750857,0.00001318767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005939662,"about_ca_system_score_gemma":0.000006301204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001937555,"about_ca_topic_score_gemma":0.000008486182,"domain_scores_codex":[0.9992949,0.00006604793,0.0001723274,0.000139013,0.000147447,0.0001802755],"domain_scores_gemma":[0.9997988,0.00003964448,0.0000360472,0.0000610325,0.00001394189,0.00005057067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001267673,0.00004799991,0.0009812362,0.000359897,0.00003778709,0.00002181436,0.001628252,0.6563859,0.2414742,0.00001359339,0.009713951,0.08932275],"study_design_scores_gemma":[0.0005900586,0.00008323205,0.0003081167,0.00002373886,0.0000204403,0.00002003578,0.0002538721,0.9941409,0.001858547,0.000241752,0.002223013,0.0002362688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960819,0.0003480719,0.001547772,0.0001055008,0.0001909647,0.0001099564,0.000006965077,0.000378906,0.001229928],"genre_scores_gemma":[0.9956446,0.000004843419,0.002559191,0.0001347014,0.00006956341,0.0001020497,0.0001864783,0.00002903658,0.001269523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.337755,"threshold_uncertainty_score":0.5241347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378851185898026,"score_gpt":0.2249850948605009,"score_spread":0.2111965830015206,"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."}}