{"id":"W4416214740","doi":"10.1109/lra.2025.3632747","title":"Fine-Grained Classification for Depth Estimation From Monocular Microscopy for Robotic Micromanipulation of Motile Cells","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Monocular; Focus (optics); Feature (linguistics); Pipette; Discriminative model; Generalization; Pattern recognition (psychology); Sperm cell","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":[0.0004353954,0.000650908,0.0003814303,0.0003804851,0.0002217219,0.0003803732,0.0008852434,0.0006637697,0.001258449],"category_scores_gemma":[0.0009715423,0.0002912492,0.000557086,0.0002799402,0.0002811186,0.0006841309,0.0009078931,0.0008388621,0.0003406766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007326867,"about_ca_system_score_gemma":0.0006731485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005791561,"about_ca_topic_score_gemma":0.00750524,"domain_scores_codex":[0.9997979,0.00001948515,0.000007915897,0.00007091751,0.00006734105,0.00003654641],"domain_scores_gemma":[0.9997519,0.00007368543,0.00004241083,0.00003745266,0.00007449062,0.00002005779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002428746,0.0001243712,0.003155124,0.0001313038,0.00005527267,0.0001379533,0.0001843786,0.1914258,0.291339,0.002663155,0.002338507,0.5082023],"study_design_scores_gemma":[0.000003561211,0.00005230297,0.0009683638,0.000004993152,0.00001067892,0.00003993655,0.00001210719,0.9693462,0.02792656,0.000818238,0.000808435,0.000008588137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06451275,0.0003304109,0.9316541,0.0001569797,0.00003967213,0.0000390034,0.00009043326,0.002274649,0.0009019507],"genre_scores_gemma":[0.6998652,0.0003265393,0.2964201,0.000233517,0.00003373686,0.00008914097,0.0002734865,0.0001231206,0.002635156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005791561,"threshold_uncertainty_score":0.01151574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919829012492094,"score_gpt":0.2790975129987001,"score_spread":0.2598992228737791,"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."}}