{"id":"W948154642","doi":"10.1007/978-3-319-16631-5_17","title":"An Abstraction for Correspondence Search Using Task-Based Controls","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Abstraction; Set (abstract data type); Task (project management); Simple (philosophy); Process (computing); Matching (statistics); Abstraction layer; Feature (linguistics); Artificial intelligence; Feature matching; Theoretical computer science; Image (mathematics); Programming language; Software","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":[0.001066268,0.0008257456,0.00104098,0.001045412,0.0008131295,0.002301635,0.002713215,0.001290666,0.01227796],"category_scores_gemma":[0.002944837,0.0006427088,0.001562417,0.001288095,0.001333237,0.003540582,0.004267272,0.001923343,0.003232832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000513646,"about_ca_system_score_gemma":0.0008910333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002851448,"about_ca_topic_score_gemma":0.002569163,"domain_scores_codex":[0.9987192,0.0002159151,0.0001336452,0.0003106146,0.0004311988,0.0001893653],"domain_scores_gemma":[0.9988486,0.0003060142,0.00005674368,0.0005704528,0.0001329496,0.00008511722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000740885,0.0002632202,0.0008807916,0.0004092783,0.0001010832,0.0005121093,0.0008825216,0.05654079,0.03804839,0.5068495,0.01066955,0.384102],"study_design_scores_gemma":[0.0000800357,0.0002287868,0.0004417747,0.00005293741,0.00008937069,0.0002824915,0.0001226096,0.6069907,0.01568889,0.3499318,0.02603096,0.00005955592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003621781,0.0001090769,0.9905217,0.00004950862,0.0000518696,0.0000605459,0.00008895656,0.002006938,0.003489739],"genre_scores_gemma":[0.2992484,0.0003995782,0.6829147,0.0001369806,0.0001335268,0.0003651819,0.0007006255,0.0008882789,0.0152128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01227796,"threshold_uncertainty_score":0.04107386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0654038300869933,"score_gpt":0.360539775056912,"score_spread":0.2951359449699187,"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."}}