{"id":"W4206928375","doi":"10.1002/cncy.22540","title":"A different kind of smart: What pathologists can learn from an octopus","year":2022,"lang":"en","type":"article","venue":"Cancer Cytopathology","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"octopus (software); Medicine; Medical physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006805159,0.0001613246,0.0005735823,0.0001513069,0.0001259248,0.00001089371,0.000210676,0.0001209427,0.002533868],"category_scores_gemma":[0.002608738,0.0001310617,0.00008170568,0.0001930425,0.0002932105,0.00007087807,0.0001891014,0.0007778049,0.000008284595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004368041,"about_ca_system_score_gemma":0.002301205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113907,"about_ca_topic_score_gemma":0.0003980144,"domain_scores_codex":[0.99729,0.0005017383,0.0003561658,0.0004228693,0.0005683904,0.0008608385],"domain_scores_gemma":[0.997081,0.0001829328,0.0001220191,0.0004970986,0.00009459229,0.002022357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002766639,0.001182183,0.3473008,0.00033991,0.00005395884,0.005062822,0.006350103,0.0000265111,0.05673783,0.0001969894,0.003889588,0.5760927],"study_design_scores_gemma":[0.007900122,0.01247774,0.827362,0.000286788,0.0001772629,0.0006473103,0.003634002,0.0002753817,0.01242224,0.001374136,0.1329663,0.0004767617],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977502,0.002812166,0.00005248153,0.01787433,0.0008977373,0.0004201591,0.0002023827,0.00004105709,0.0001976901],"genre_scores_gemma":[0.9834845,0.001487015,0.00006000747,0.01381618,0.0002701954,0.000239887,0.0001300124,0.00002551443,0.000486701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5756159,"threshold_uncertainty_score":0.998378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1219270582588384,"score_gpt":0.4268831425198488,"score_spread":0.3049560842610104,"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."}}