{"id":"W2105661584","doi":"10.1103/physreve.86.011117","title":"Corrections to scaling for watersheds, optimal path cracks, and bridge lines","year":2012,"lang":"en","type":"article","venue":"Physical Review E","topic":"Theoretical and Computational Physics","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Scaling; Exponent; Path (computing); Conjecture; Fractal; Bridge (graph theory); Dimension (graph theory); Scaling limit; Line (geometry); Fractal dimension; Omega; Mathematics; Heuristic; Limit (mathematics); Physics; Mathematical analysis; Geometry; Combinatorics; Quantum mechanics; Computer science; Mathematical optimization","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":[0.0007169631,0.0006334127,0.0005268852,0.001400465,0.001150866,0.001128966,0.001434446,0.001464768,0.003726627],"category_scores_gemma":[0.007445923,0.0003689143,0.001097943,0.0004422842,0.001926965,0.002450488,0.001250485,0.001134316,0.0002566218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001603688,"about_ca_system_score_gemma":0.0006189154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003266696,"about_ca_topic_score_gemma":0.002829605,"domain_scores_codex":[0.9997042,0.0000599224,0.00001432732,0.00005542524,0.00008393628,0.00008221193],"domain_scores_gemma":[0.9981652,0.0006746284,0.0003937982,0.0002658901,0.0002197456,0.0002807781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001219184,0.00008617589,0.006591909,0.0001660837,0.00006125384,0.0009093654,0.0004775035,0.2544896,0.01276861,0.7101831,0.004063867,0.01008058],"study_design_scores_gemma":[0.00003731134,0.00004868753,0.0027432,0.00002954961,0.00001807362,0.0002330558,0.000134933,0.7214427,0.001981932,0.2717448,0.001540741,0.0000450332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8715391,0.001198717,0.1039137,0.002313558,0.0004092297,0.00007149549,0.000161367,0.0006768896,0.01971595],"genre_scores_gemma":[0.9898329,0.0002799137,0.006870945,0.000109905,0.00008559526,0.00003753987,0.00006928451,0.0001141145,0.002599836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003726627,"threshold_uncertainty_score":0.01246685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885635880384892,"score_gpt":0.3174728780711483,"score_spread":0.2986165192672994,"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."}}