{"id":"W2056710678","doi":"10.1007/bf03037573","title":"From eager to lazy constrained data acquisition: A general framework","year":2001,"lang":"en","type":"article","venue":"New Generation Computing","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Patient Safety Institute","keywords":"Computer science; Lazy evaluation; Programming language; Functional programming","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00435662,0.001143876,0.002108238,0.00183726,0.002167145,0.006900113,0.006113401,0.002732884,0.008217815],"category_scores_gemma":[0.01321469,0.001450466,0.002082472,0.003567525,0.007338837,0.01478505,0.008276595,0.005768581,0.001237844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001979369,"about_ca_system_score_gemma":0.003576669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006772179,"about_ca_topic_score_gemma":0.008995689,"domain_scores_codex":[0.9962967,0.001130643,0.0002812413,0.0008211588,0.001022831,0.0004474832],"domain_scores_gemma":[0.9924279,0.002820621,0.0003155719,0.003315323,0.0008410669,0.0002795548],"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":[0.00005166379,0.00003475135,0.0001525215,0.0001021581,0.00003479642,0.0001032962,0.0001533806,0.02714786,0.000857847,0.9415693,0.002373429,0.0274189],"study_design_scores_gemma":[0.00001392394,0.00001418814,0.00005154284,0.0000278179,0.00002264471,0.00006414316,0.00003786335,0.1550555,0.0007234687,0.8388792,0.005086647,0.00002301167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001734619,0.0004791403,0.9931338,0.0005813781,0.00004616677,0.00004685865,0.00006048862,0.0003217305,0.003595849],"genre_scores_gemma":[0.2385373,0.002460425,0.7435664,0.001096303,0.0005689226,0.000358576,0.0003433634,0.001128895,0.01193974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008217815,"threshold_uncertainty_score":0.02749133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05510647354240323,"score_gpt":0.3076926848245852,"score_spread":0.252586211282182,"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."}}