{"id":"W2467443835","doi":"","title":"Vector-raster interoperation: a spatial query language approach","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Raster graphics; Computer science; Query language; Interoperation; Spatial query; Spatial analysis; Interface (matter); SQL; Raster data; Database; Query by Example; Information retrieval; Programming language; Sargable; Web search query; Geography; Artificial intelligence; World Wide Web; Remote sensing; Interoperability; Search engine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006806555,0.0007432831,0.0008544181,0.0021584,0.0009219227,0.0051371,0.003294249,0.001064806,0.006162184],"category_scores_gemma":[0.006056477,0.0006081918,0.001470203,0.002413721,0.002076488,0.00856137,0.004422369,0.001977512,0.001996337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145159,"about_ca_system_score_gemma":0.00202803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003219774,"about_ca_topic_score_gemma":0.003193938,"domain_scores_codex":[0.9955791,0.001738191,0.0006693961,0.0005617475,0.001195912,0.0002556642],"domain_scores_gemma":[0.9966599,0.001465109,0.0002269944,0.0005928526,0.0008844078,0.0001707516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002341488,0.0001680703,0.001875111,0.0005380794,0.0001592011,0.0004277328,0.002730002,0.01264893,0.01281178,0.8263167,0.02285323,0.119237],"study_design_scores_gemma":[0.0001428472,0.0003280574,0.0008600638,0.0002960054,0.0002388207,0.0009402306,0.002069575,0.2502598,0.03883135,0.3414595,0.3643779,0.0001958763],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002464247,0.0001484571,0.9872029,0.0007841687,0.00004779732,0.0001144616,0.0004109165,0.004129594,0.004697435],"genre_scores_gemma":[0.07651576,0.0004178533,0.9130697,0.0008646909,0.000115129,0.0002913636,0.001809746,0.001609878,0.005305817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006806555,"threshold_uncertainty_score":0.03599691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008695249768394744,"score_gpt":0.2537771704429636,"score_spread":0.2450819206745689,"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."}}