{"id":"W4379622008","doi":"10.1016/j.ijthermalsci.2023.108453","title":"Inverse heat transfer prediction of the thermal parameters of tumors during cryosurgery","year":2023,"lang":"en","type":"article","venue":"International Journal of Thermal Sciences","topic":"Thermoelastic and Magnetoelastic Phenomena","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cryosurgery; Bioheat transfer; Initialization; Inverse; Materials science; Inverse problem; Heat transfer; Thermodynamics; Thermal; Mechanics; Biomedical engineering; Physics; Mathematics; Computer science; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003719301,0.00007544547,0.0001342645,0.0001738364,0.00003730828,0.00001399192,0.0005038708,0.0000240301,0.00007829077],"category_scores_gemma":[0.00005396637,0.00004826681,0.0001324143,0.0002473936,0.0002403179,0.0002207347,0.00003511106,0.0000999702,0.000002639035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003423821,"about_ca_system_score_gemma":0.00005066269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001508374,"about_ca_topic_score_gemma":0.000002078926,"domain_scores_codex":[0.9988299,0.00003073869,0.0004055503,0.00005999167,0.0005443993,0.0001293963],"domain_scores_gemma":[0.9996332,0.0001285625,0.00005903959,0.00005727896,0.00008347729,0.0000384417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005422872,0.00002289925,0.0122928,0.00001861603,0.0001086378,0.000007873516,0.0008917356,0.4869613,0.4981691,0.0001144061,0.00005780442,0.001300559],"study_design_scores_gemma":[0.0008726001,0.0002075819,0.7918466,0.0004179495,0.00005363997,0.00008544015,0.0008723642,0.03745458,0.167515,0.0004470634,0.00007100644,0.0001562206],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969814,0.0000416151,0.0002480223,0.0001221006,0.001650516,0.00003769367,0.00001982402,0.00001880936,0.0008800414],"genre_scores_gemma":[0.999758,0.00002449554,0.00006991503,0.00001180389,0.0001057773,0.000001046337,3.0792e-7,0.000006620494,0.000022001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7795538,"threshold_uncertainty_score":0.1968264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650405440367755,"score_gpt":0.2105272923179658,"score_spread":0.1940232379142882,"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."}}