{"id":"W2145850473","doi":"","title":"Qualitative & Semi-Quantitative Reasoning Techniques for Engineering Projects at Conceptual Stage","year":2003,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Qualitative reasoning; Computer science; Abstraction; Management science; Artificial intelligence; Engineering; Epistemology","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003678727,0.0003771136,0.0006888997,0.0005853692,0.0004405561,0.001070634,0.002460089,0.0001284743,0.0003339687],"category_scores_gemma":[0.001808014,0.0003594102,0.0001747882,0.0009961727,0.0001218518,0.002981951,0.000636243,0.0004080435,0.000005633916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900726,"about_ca_system_score_gemma":0.0002742436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001612026,"about_ca_topic_score_gemma":0.000008398451,"domain_scores_codex":[0.9969215,0.0005542589,0.0007487283,0.0006331005,0.0005585473,0.0005837969],"domain_scores_gemma":[0.9956464,0.002321086,0.0008659366,0.0004781936,0.0004670453,0.0002212938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000743911,0.0006617976,0.1006878,0.001269356,0.00140995,0.0002387257,0.1475601,0.01211449,0.3193819,0.3066421,0.09358356,0.01570629],"study_design_scores_gemma":[0.002268895,0.0003650794,0.008184688,0.005093218,0.000140979,0.00007624147,0.007410883,0.03217778,0.8099506,0.01577974,0.115295,0.003256821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680067,0.01075643,0.8143566,0.0001074657,0.0005207596,0.001369399,0.00009712182,0.0003126597,0.004472898],"genre_scores_gemma":[0.7460275,0.0005399764,0.2517988,0.0002459976,0.00007354864,0.0002487918,0.00001824794,0.00007525661,0.0009718452],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5780208,"threshold_uncertainty_score":0.9999663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3220043061080372,"score_gpt":0.5632870592119252,"score_spread":0.2412827531038881,"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."}}