{"id":"W1534908681","doi":"10.1023/a:1019718019825","title":"Semi-Automated Extraction of Rivers from Digital Imagery","year":2002,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Panchromatic film; Computer science; Channel (broadcasting); Artificial intelligence; Aerial imagery; Satellite imagery; Computer vision; Feature extraction; Process (computing); Remote sensing; Image resolution; Resolution (logic); Pattern recognition (psychology); Geography; Telecommunications","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":[0.0003585525,0.0009295182,0.0009371025,0.003123035,0.0004713635,0.001257734,0.0008464028,0.0006289057,0.00296468],"category_scores_gemma":[0.001167153,0.0006428889,0.0009047152,0.001882848,0.0003518146,0.0009101393,0.000763842,0.0005240801,0.002710351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245238,"about_ca_system_score_gemma":0.001073204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005140128,"about_ca_topic_score_gemma":0.01244001,"domain_scores_codex":[0.9995715,0.00005868011,0.00003400727,0.0001035522,0.000167828,0.00006453287],"domain_scores_gemma":[0.9993394,0.000220006,0.00006975341,0.0001473904,0.0001987046,0.000024644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002432052,0.0001592759,0.003070314,0.0005336495,0.0001295932,0.0002589978,0.0001494352,0.02518648,0.1170508,0.001901502,0.009435675,0.841881],"study_design_scores_gemma":[0.0001097941,0.0002421628,0.03292891,0.0001671158,0.0003336807,0.00125924,0.0004056229,0.7201256,0.1899253,0.007521468,0.04685079,0.0001303571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07160723,0.0008266297,0.9059876,0.0001398534,0.00009059719,0.0002445074,0.003278287,0.01320461,0.004620736],"genre_scores_gemma":[0.2392288,0.0006695691,0.7422026,0.0000837829,0.00005385094,0.0002037664,0.01048185,0.0004989477,0.006576861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005140128,"threshold_uncertainty_score":0.01022041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006675795221587271,"score_gpt":0.1927596914476015,"score_spread":0.1860838962260143,"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."}}