{"id":"W4281673866","doi":"10.1145/3529372.3533280","title":"Integration of text and geospatial search for hydrographic datasets using the lucene search library","year":2022,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Geospatial analysis; Computer science; Information retrieval; Hydrography; Index (typography); Metadata; World Wide Web; Geography; Cartography","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":[0.00147065,0.001497727,0.001328318,0.009083354,0.00115584,0.003339936,0.001830177,0.0009393051,0.02880616],"category_scores_gemma":[0.006664292,0.0006390148,0.001507421,0.005870523,0.0006207654,0.006860599,0.0040526,0.001100162,0.02013248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279841,"about_ca_system_score_gemma":0.001459153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01319315,"about_ca_topic_score_gemma":0.02383582,"domain_scores_codex":[0.99819,0.0002339354,0.0002622999,0.0004082581,0.0007855153,0.0001201247],"domain_scores_gemma":[0.997274,0.001425713,0.0001954697,0.0004153028,0.0005103556,0.0001791858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00121236,0.0004806458,0.006797211,0.005186234,0.000721617,0.002317645,0.002784161,0.008707933,0.03195687,0.02524499,0.5522217,0.3623687],"study_design_scores_gemma":[0.0003079748,0.0002352837,0.01244546,0.0007096996,0.0002337872,0.001768213,0.001565017,0.1242409,0.06246572,0.03037455,0.7650021,0.0006512422],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.02443917,0.001844888,0.3181955,0.001515356,0.0002250487,0.0008336607,0.1317586,0.4693602,0.05182761],"genre_scores_gemma":[0.1285529,0.001928869,0.4522252,0.001514736,0.0002387612,0.001414723,0.3378042,0.03877917,0.03754146],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02880616,"threshold_uncertainty_score":0.09636617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03109530321711497,"score_gpt":0.3101144590443358,"score_spread":0.2790191558272208,"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."}}