{"id":"W6958413058","doi":"10.6084/m9.figshare.29365718.v2","title":"Extracting and classifying spatial language terms from planning documents","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Pipeline (software); Process (computing); Urban planning; Plan (archaeology); Domain (mathematical analysis); Expression (computer science); Natural language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001287427,0.0009142073,0.0003482824,0.00675955,0.0008027825,0.001839001,0.0008745343,0.0006803248,0.003347538],"category_scores_gemma":[0.004974815,0.0002793302,0.0006409556,0.003644492,0.000709641,0.001761232,0.00129258,0.0008904066,0.002150575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001809589,"about_ca_system_score_gemma":0.002307734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01490001,"about_ca_topic_score_gemma":0.01460233,"domain_scores_codex":[0.9988218,0.0003363274,0.0001329546,0.0002909084,0.0003062775,0.0001116699],"domain_scores_gemma":[0.9969233,0.001800462,0.0002845277,0.0002611726,0.00065098,0.00007951302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004144073,0.0003129829,0.02902337,0.00146884,0.00008694264,0.002479066,0.007312381,0.01773336,0.05140487,0.01774414,0.03798212,0.8340377],"study_design_scores_gemma":[0.0001722962,0.0003471886,0.04532561,0.0004457926,0.0002428728,0.002481613,0.01611627,0.6361017,0.1144573,0.03209155,0.1520173,0.0002005742],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5179852,0.002002937,0.4031433,0.002315126,0.0002188695,0.002687619,0.03078375,0.02051971,0.02034344],"genre_scores_gemma":[0.5587044,0.0005821867,0.4093902,0.0001782037,0.00007589962,0.0005927733,0.02575436,0.0005020835,0.004219979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01490001,"threshold_uncertainty_score":0.02962655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03740844534465047,"score_gpt":0.3596898732052115,"score_spread":0.322281427860561,"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."}}