{"id":"W4298858945","doi":"10.17615/tb72-kv48","title":"Efficient algorithms for line and curve segment intersection using restricted predicates","year":2021,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Intersection (aeronautics); Line (geometry); Line segment; Algorithm; Computer science; Mathematics; Artificial intelligence; Geometry; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002633922,0.0001023477,0.0001446933,0.00006834587,0.00005895939,0.000057774,0.00004271861,0.00004899082,0.00001244202],"category_scores_gemma":[0.00007231187,0.00009536141,0.00003962792,0.000280094,0.0000452384,0.0001152012,0.00005418048,0.00007259069,5.334414e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003899883,"about_ca_system_score_gemma":0.00001445063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005978172,"about_ca_topic_score_gemma":0.00000153852,"domain_scores_codex":[0.9994528,0.0000123613,0.0001630711,0.0001589278,0.00007719112,0.000135648],"domain_scores_gemma":[0.999699,0.00008090764,0.00002553402,0.0001074118,0.00004028672,0.00004689103],"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":[0.0001317384,0.0003446736,0.001963234,0.0007542084,0.0007752896,0.00004759988,0.001967931,0.6738991,0.1776649,0.02004183,0.002762982,0.1196465],"study_design_scores_gemma":[0.0001411582,0.00004095597,0.0001309046,0.00003469451,0.00003465859,0.000004892782,0.0001183698,0.7214773,0.2682952,0.008334454,0.001266435,0.0001210012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07001384,0.001123604,0.9279172,0.0000964857,0.0001031905,0.0001209338,0.00001696894,0.0005291494,0.00007864018],"genre_scores_gemma":[0.4202107,0.00003949046,0.5793,0.0000520817,0.0001443637,0.0000409145,0.00007130564,0.00003819886,0.0001029102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3501969,"threshold_uncertainty_score":0.3888726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02166177826750226,"score_gpt":0.253375139401551,"score_spread":0.2317133611340488,"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."}}