{"id":"W1872081294","doi":"10.1002/rob.21590","title":"What is a Hole? Discovering Access Holes in Disaster Rubble with Functional and Photometric Attributes","year":2015,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Rubble; Metric (unit); Computer science; Set (abstract data type); Search and rescue; Traverse; Artificial intelligence; Engineering; Civil engineering; Cartography; Geography","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":[0.0002823238,0.0003416812,0.0004946749,0.001619326,0.000409066,0.0008880165,0.0005058231,0.0006657306,0.0005458289],"category_scores_gemma":[0.001986647,0.000257911,0.0003468662,0.000733863,0.0008638691,0.001941549,0.001144935,0.0003448641,0.0001695029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002142237,"about_ca_system_score_gemma":0.0002762048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002143116,"about_ca_topic_score_gemma":0.004164268,"domain_scores_codex":[0.9997147,0.00002924088,0.00001405296,0.00006865533,0.00009470316,0.00007854485],"domain_scores_gemma":[0.999208,0.0002333088,0.0002092775,0.00007913497,0.0001859252,0.00008432232],"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":[0.001200653,0.000257471,0.2457419,0.000928649,0.0001139423,0.004269105,0.004408841,0.04549512,0.2216324,0.01178845,0.004283669,0.4598798],"study_design_scores_gemma":[0.00003398452,0.000597566,0.2386075,0.0002429727,0.0001244848,0.007299204,0.01054806,0.64554,0.06338068,0.02328074,0.01018172,0.0001631521],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7642611,0.0004778176,0.2316736,0.0001730839,0.00002746885,0.00009631531,0.0002435176,0.0004705811,0.002576445],"genre_scores_gemma":[0.9665238,0.0001128374,0.0328407,0.00002173278,0.00001013854,0.00001362226,0.000124637,0.00002338024,0.0003292145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002143116,"threshold_uncertainty_score":0.004261315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03859045935320986,"score_gpt":0.2525203290909072,"score_spread":0.2139298697376973,"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."}}