{"id":"W2749066189","doi":"10.1103/physreve.96.023307","title":"Versatile and efficient pore network extraction method using marker-based watershed segmentation","year":2017,"lang":"en","type":"article","venue":"Physical review. E","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":394,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Porosity; Computer science; Porous medium; Materials science; Algorithm; Permeability (electromagnetism); Anisotropy; Ranging; Biological system; Optics; Composite material; Physics","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":[0.000598656,0.000659946,0.0006445816,0.001566234,0.0004905685,0.001101197,0.001302914,0.0008843776,0.002347639],"category_scores_gemma":[0.001456712,0.000529422,0.0006360863,0.00113905,0.0004946353,0.00126953,0.0008808924,0.001022336,0.0009901875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006859141,"about_ca_system_score_gemma":0.001382098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002289884,"about_ca_topic_score_gemma":0.003476755,"domain_scores_codex":[0.9996955,0.00002676241,0.00002182056,0.0000785489,0.0001425446,0.00003478436],"domain_scores_gemma":[0.9995672,0.0001617214,0.00005995843,0.0000623119,0.0001238922,0.00002491458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001808912,0.0001111489,0.001746773,0.0003294565,0.00008319357,0.0002863121,0.0003663514,0.09360943,0.3953377,0.0160235,0.00384779,0.4880775],"study_design_scores_gemma":[0.00002304268,0.00003105185,0.0007722278,0.00001208073,0.00002127637,0.0001940645,0.00004878466,0.8243872,0.1628572,0.004988146,0.006628585,0.00003638488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0106605,0.00004241848,0.9866452,0.00005299747,0.000009771046,0.00005309314,0.00008557931,0.001889718,0.0005608055],"genre_scores_gemma":[0.06148314,0.00008200442,0.936755,0.00002147899,0.000007373088,0.00008996003,0.000273552,0.000426845,0.0008606054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002347639,"threshold_uncertainty_score":0.007853687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271225686505144,"score_gpt":0.3684144794747883,"score_spread":0.3457022226097368,"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."}}