{"id":"W4411064948","doi":"10.1088/1742-6596/3022/1/012001","title":"Deep Learning-driven Blind Spot Detection for Forklifts in Industrial Environments","year":2025,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carbon Engineering (Canada)","funders":"","keywords":"Blind spot; Artificial intelligence; Computer science; Deep learning; Process engineering; Engineering","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":[0.0003473609,0.0007177599,0.0005685854,0.000565304,0.0002424477,0.0004155249,0.00101237,0.0005889595,0.001536483],"category_scores_gemma":[0.0008967504,0.0002734827,0.0003157113,0.00026714,0.0002400034,0.0006049959,0.0007529201,0.0007595576,0.0006141321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004707331,"about_ca_system_score_gemma":0.0007510465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004143211,"about_ca_topic_score_gemma":0.006315232,"domain_scores_codex":[0.9997477,0.00002405793,0.000007472636,0.00007310334,0.00009649927,0.00005106318],"domain_scores_gemma":[0.9997009,0.00006942041,0.0000362579,0.00002890423,0.0001342145,0.00003032895],"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":[0.0007182388,0.0004706747,0.007297049,0.0002223846,0.000108664,0.0004259704,0.0001763532,0.2449577,0.1163569,0.001115054,0.008878481,0.6192726],"study_design_scores_gemma":[0.000008368537,0.00006257983,0.00130235,0.000007566388,0.000007322609,0.00004316063,0.00001527604,0.9857169,0.01179042,0.0005110128,0.0005271153,0.00000785982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2195149,0.0005173694,0.7676448,0.0002305335,0.0001585913,0.00008992393,0.0002705669,0.008728465,0.002844737],"genre_scores_gemma":[0.8716086,0.0001364752,0.1237045,0.0001609515,0.00003165095,0.00004281425,0.0004178453,0.0001453715,0.003751753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004143211,"threshold_uncertainty_score":0.008238196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03145573844024943,"score_gpt":0.2468992617610787,"score_spread":0.2154435233208292,"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."}}