{"id":"W2152315047","doi":"10.1145/2502524.2502566","title":"Hexacopters for everyone","year":2013,"lang":"en","type":"article","venue":"","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lagging; Computer science; Empirical research; Management science; Engineering management; Engineering; Epistemology; Mathematics","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":[0.00001558457,0.0000671295,0.00005587056,0.0000264209,0.00001318595,0.00001906255,0.00005491731,0.00002500008,0.0003928604],"category_scores_gemma":[0.000005778565,0.00006642775,0.00002616295,0.00003431122,0.000005471749,0.0001063473,0.00000764417,0.00003982218,0.0003380062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003050291,"about_ca_system_score_gemma":9.085954e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000746565,"about_ca_topic_score_gemma":2.565078e-7,"domain_scores_codex":[0.9996887,0.000001107598,0.00006772295,0.00005802642,0.0000372004,0.0001472122],"domain_scores_gemma":[0.9998449,0.00002423449,0.000002989378,0.00008082993,0.00000850063,0.00003855358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001400923,0.00001163469,0.0004304797,0.00009243452,0.00005687545,5.173551e-7,0.0002321504,0.3926093,0.5105162,0.0006886881,0.09057643,0.004783818],"study_design_scores_gemma":[0.000447628,0.00005055359,0.002764639,0.00001926127,0.000005072461,0.000004471028,0.0001802343,0.7423126,0.1695685,0.0001024078,0.08413438,0.0004102572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3808236,0.0009876856,0.5207554,0.0000515484,0.00121204,0.00077615,0.000002323196,0.003183735,0.09220754],"genre_scores_gemma":[0.9585458,0.00000339665,0.03835938,0.00003792436,0.00005476774,0.0001400029,0.00000292299,0.00003373428,0.002822054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5777222,"threshold_uncertainty_score":0.4344498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003078295439755417,"score_gpt":0.1678823206908822,"score_spread":0.1648040252511268,"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."}}