{"id":"W3162387593","doi":"10.1039/d1ra02869c","title":"Rare bioparticle detection <i>via</i> deep metric learning","year":2021,"lang":"en","type":"article","venue":"RSC Advances","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"Ministry of Education, India; Ministry of Education - Singapore; National Research Foundation Singapore; National Research Foundation","keywords":"Metric (unit); Artificial intelligence; Deep neural networks; Deep learning; Artificial neural network; Computer science; Simple (philosophy); Pattern recognition (psychology); Machine learning; Engineering; Philosophy","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":[0.0004946791,0.0005632178,0.00043859,0.0006491972,0.0001630601,0.0005274985,0.0007394246,0.0007047058,0.0009133283],"category_scores_gemma":[0.001079904,0.0001642531,0.0003424183,0.0004622915,0.0002415721,0.0005549825,0.0005782885,0.0005126948,0.0004915333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005089159,"about_ca_system_score_gemma":0.0003766907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056734,"about_ca_topic_score_gemma":0.002833101,"domain_scores_codex":[0.9997211,0.00004264471,0.00001318029,0.00008229693,0.0001099945,0.00003068998],"domain_scores_gemma":[0.9997858,0.0000495661,0.000054179,0.0000183658,0.00007342922,0.00001862952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009217971,0.000299369,0.01625264,0.0007983532,0.0001895888,0.001324445,0.0001588232,0.07827613,0.2930481,0.007537101,0.01479829,0.5863954],"study_design_scores_gemma":[0.00001327228,0.000191372,0.00237207,0.00001509256,0.00002724581,0.0004038581,0.00002552932,0.8765137,0.1146851,0.002372667,0.003343469,0.00003657652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3239289,0.003111616,0.6583428,0.0009139202,0.0004068831,0.000142286,0.001196676,0.004643918,0.007313126],"genre_scores_gemma":[0.8674043,0.000758272,0.1249607,0.0003527066,0.00007028085,0.00006741751,0.0006774023,0.00007125443,0.005637692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002056734,"threshold_uncertainty_score":0.004089475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008272986701432996,"score_gpt":0.2381783762499563,"score_spread":0.2299053895485233,"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."}}