{"id":"W2039256019","doi":"10.5220/0005210200870098","title":"Introducing the Φ-Descriptor - A Most Versatile Relative Position Descriptor","year":2015,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Position (finance); Artificial intelligence; Categorization; Histogram; Pattern recognition (psychology); Basis (linear algebra); Computer science; Computer vision; Strengths and weaknesses; Texture (cosmology); Boundary (topology); Image (mathematics); Mathematics; Geometry","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":[0.0003029126,0.00008796974,0.00007221936,0.0000625091,0.0001598993,0.0002074514,0.000256464,0.00003793275,0.000121535],"category_scores_gemma":[0.0001501528,0.00006202644,0.00002955064,0.0003714925,0.00004816117,0.001092378,0.00009054761,0.0001120967,0.0001523444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419222,"about_ca_system_score_gemma":0.0001259079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001186937,"about_ca_topic_score_gemma":0.00003962811,"domain_scores_codex":[0.9991528,0.00009157127,0.0001523438,0.0002338078,0.0002238149,0.0001456976],"domain_scores_gemma":[0.9992942,0.00005344914,0.00006220412,0.0003257833,0.000171649,0.00009267329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003683765,0.00008346201,0.001706702,0.000005585975,0.00006789233,0.000008999631,0.01736713,0.00869116,0.002301621,0.7420682,0.1263409,0.1013215],"study_design_scores_gemma":[0.001446331,0.0003320308,0.004098515,0.00003089588,0.0000294089,0.00009655413,0.002653266,0.9504609,0.002883208,0.01249304,0.02498286,0.0004930226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002369713,0.00002725177,0.9715559,0.007223336,0.0008893224,0.0001706668,0.000001354251,0.0002118331,0.01755064],"genre_scores_gemma":[0.8642024,0.000004255329,0.1317422,0.001760398,0.0001556502,0.00001640424,0.000008226837,0.000008360003,0.00210211],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9417697,"threshold_uncertainty_score":0.2529365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232938440001465,"score_gpt":0.2290978164908825,"score_spread":0.205803972490736,"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."}}