{"id":"W4414538412","doi":"10.1109/tim.2025.3614904","title":"Robotic Grasp Detection via Residual Efficient Channel Attention and Multiscale Feature Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Key Research and Development Program of Sichuan Province; National Natural Science Foundation of China","keywords":"GRASP; Upsampling; Feature (linguistics); Benchmark (surveying); Residual; Convolution (computer science); Kernel (algebra); Focus (optics); Object detection","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":[0.0005294071,0.0009023405,0.0008835673,0.0008460911,0.0002418876,0.0006063357,0.001154106,0.0007181854,0.001826185],"category_scores_gemma":[0.001678744,0.0003627737,0.0007396301,0.0005985438,0.0005766673,0.001243016,0.001517143,0.0006455816,0.0006222693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005181844,"about_ca_system_score_gemma":0.0007840964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00250658,"about_ca_topic_score_gemma":0.003592771,"domain_scores_codex":[0.9996063,0.00003968423,0.00001575413,0.0001374119,0.0001253738,0.00007550325],"domain_scores_gemma":[0.9994931,0.0001551418,0.00009996245,0.0001060167,0.000104215,0.00004144188],"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":[0.0002624882,0.0001492137,0.001877212,0.0001451363,0.0001099467,0.0001990791,0.00009051287,0.1728819,0.1114843,0.00490363,0.00373939,0.7041572],"study_design_scores_gemma":[0.000009890899,0.00009813005,0.001275773,0.000008442009,0.00002222765,0.0001222637,0.00001326671,0.9733653,0.02045196,0.003670159,0.0009429119,0.00001979141],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03380819,0.0002205926,0.9620672,0.00008784667,0.00002941962,0.00003545693,0.00007125222,0.002445851,0.00123425],"genre_scores_gemma":[0.7223222,0.000277323,0.2720728,0.0002343866,0.00005837132,0.0001109172,0.0003348312,0.0002535513,0.004335704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00250658,"threshold_uncertainty_score":0.006109238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168453070492619,"score_gpt":0.2233460524727411,"score_spread":0.2065007454234792,"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."}}