{"id":"W2600954308","doi":"","title":"The business end of objects: Monitoring object orientation","year":2009,"lang":"en","type":"article","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Miami","keywords":"Orientation (vector space); Object (grammar); Computer science; Computer vision; Artificial intelligence; Business; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001908177,0.0001231903,0.0001228932,0.00003591012,0.0003595996,0.0002167988,0.0001499214,0.00006214644,0.0001545655],"category_scores_gemma":[0.00001381135,0.0000794119,0.00003731617,0.000377525,0.00005437726,0.006085795,0.00001212726,0.0001207791,0.00001830018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001320116,"about_ca_system_score_gemma":0.00002412376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.252681e-7,"about_ca_topic_score_gemma":0.00000450782,"domain_scores_codex":[0.9990475,0.00007893289,0.0003602162,0.00009351509,0.0001655091,0.0002543767],"domain_scores_gemma":[0.9994926,0.0001086886,0.0001661677,0.0001251411,0.00003245545,0.00007494965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004077617,0.00002486055,0.2096549,0.00008717497,0.00003937188,0.000002552784,0.001787873,0.007105398,0.00001380719,0.04398225,0.002423526,0.7344706],"study_design_scores_gemma":[0.0003456204,0.00009433062,0.934663,0.00009995295,0.000006173062,0.000009282829,0.0001084863,0.002006689,0.0001279479,0.0006744493,0.06171602,0.0001480635],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2607665,0.001870521,0.0001610374,0.0007425614,0.001673813,0.0003250169,0.00008351899,0.0001607137,0.7342163],"genre_scores_gemma":[0.9962571,0.002128425,0.0002711345,0.0004096042,0.0003268601,9.225984e-7,0.0004439199,0.000001919699,0.0001601523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7354906,"threshold_uncertainty_score":0.4412055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009281678288577271,"score_gpt":0.194465003906926,"score_spread":0.1851833256183487,"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."}}