{"id":"W7106669987","doi":"10.1007/978-3-319-23519-6_1670-1","title":"Using Geospatial Analytics to Understand Food Access","year":2025,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Geospatial analysis; Analytics; Data access; Data analysis; Field (mathematics); Big data","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004554966,0.0002105866,0.000458846,0.0003920731,0.0003513267,0.00007813112,0.0005771925,0.0003129722,0.002167525],"category_scores_gemma":[0.0002879231,0.000235662,0.0002466311,0.0002439789,0.0002629333,0.0001120689,0.0001239927,0.0002159587,0.00002054375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362953,"about_ca_system_score_gemma":0.00125901,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007594725,"about_ca_topic_score_gemma":0.09391291,"domain_scores_codex":[0.9982627,0.00006065343,0.0004602617,0.0003822425,0.0005947949,0.0002393591],"domain_scores_gemma":[0.9986656,0.0002503305,0.0002469906,0.0003973295,0.0002816455,0.0001581076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008064519,0.0002112548,0.002542524,0.0005923696,0.001576348,0.00001710563,0.03562036,0.004728774,0.000003360756,0.8485277,0.02846892,0.07763069],"study_design_scores_gemma":[0.0002813982,0.0001510005,0.000242397,0.0005070135,0.001212977,1.164818e-7,0.002636518,0.0004971716,0.00001033443,0.08706871,0.9065416,0.0008507086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0006930574,0.0001831575,0.01312837,0.0005344438,0.0003244226,0.0003507994,0.0001257499,0.00003432947,0.9846257],"genre_scores_gemma":[0.1332657,0.001542282,0.0009510508,0.0003760202,0.001239645,0.000006448293,0.00007591418,0.00004371321,0.8624992],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8780727,"threshold_uncertainty_score":0.9990138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0774412749683346,"score_gpt":0.3536771196528984,"score_spread":0.2762358446845638,"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."}}