{"id":"W4411809225","doi":"10.1017/s0890060425100048","title":"Managing combinatorial design challenges using flexibility and pathfinding algorithms","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"VINNOVA","keywords":"Pathfinding; Flexibility (engineering); Computer science; Algorithm; Theoretical computer science; Mathematics; Shortest path problem","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":[0.00226077,0.001634702,0.00114764,0.002320322,0.001094622,0.00207869,0.001538157,0.001674644,0.003742701],"category_scores_gemma":[0.006471913,0.0009820318,0.001486714,0.001630806,0.001844753,0.002419089,0.003034967,0.001553246,0.0004988793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097463,"about_ca_system_score_gemma":0.00128062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001895666,"about_ca_topic_score_gemma":0.002427808,"domain_scores_codex":[0.998885,0.0005237457,0.00005846972,0.0001730989,0.0002513654,0.0001083367],"domain_scores_gemma":[0.9961947,0.002788566,0.0003535181,0.0003087399,0.0002213768,0.0001330406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003701034,0.00005236345,0.0006178286,0.00009469714,0.00003894677,0.0001456912,0.0001143175,0.9272263,0.001767462,0.02829665,0.0004942436,0.04111448],"study_design_scores_gemma":[0.00001709379,0.00006957773,0.00009774767,0.00002719757,0.00001761915,0.00006977787,0.00006374723,0.9651542,0.0006910065,0.0319454,0.001833501,0.0000130855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05463592,0.0004545577,0.9362094,0.0005123115,0.000038354,0.0001565456,0.00004886995,0.0003431623,0.007600745],"genre_scores_gemma":[0.4179662,0.0004478638,0.5776132,0.0001119637,0.00003833199,0.0003281193,0.000120461,0.0001675244,0.003206354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003742701,"threshold_uncertainty_score":0.01252055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08894684844875378,"score_gpt":0.3131662669326104,"score_spread":0.2242194184838566,"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."}}