{"id":"W4245784048","doi":"10.32920/ryerson.14644827","title":"Participatory mapping of regional food assets using volunteered geographic information systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Culinary Culture and Tourism","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Volunteered geographic information; Crowdsourcing; Citizen journalism; Food security; Participatory action research; Citizen science; Business; Sustainability; Food systems; Knowledge management; Geography; Data science; Computer science; World Wide Web; Economic growth; Economics","routes":{"ca_aff":false,"ca_fund":true,"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":[0.009701048,0.0007071013,0.0006037031,0.003413371,0.002980589,0.004808276,0.002073096,0.001107312,0.003286588],"category_scores_gemma":[0.01592614,0.0004590043,0.0009413513,0.004895577,0.002633199,0.004459378,0.008000568,0.0009533047,0.0006133629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002578926,"about_ca_system_score_gemma":0.004553345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01345591,"about_ca_topic_score_gemma":0.02332148,"domain_scores_codex":[0.9889174,0.007866306,0.0003333791,0.001376387,0.001049709,0.000456857],"domain_scores_gemma":[0.9839727,0.009127463,0.001163639,0.00368925,0.001409451,0.000637606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004741,0.0004825594,0.03938896,0.002003276,0.0004254777,0.00256341,0.1167231,0.1036753,0.01063379,0.1342649,0.01573219,0.573633],"study_design_scores_gemma":[0.0002646011,0.0005806296,0.02720329,0.001347585,0.0002877704,0.0007721219,0.1267525,0.2485138,0.009683115,0.2443272,0.3399052,0.0003622588],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2213755,0.0009418849,0.6883957,0.003913547,0.0002222259,0.002917401,0.00286557,0.001414312,0.0779539],"genre_scores_gemma":[0.6621119,0.0004689049,0.3300938,0.0001840605,0.00004208133,0.001330132,0.001187057,0.0001368686,0.004445138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01345591,"threshold_uncertainty_score":0.05130464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1016706956013393,"score_gpt":0.251827965020969,"score_spread":0.1501572694196298,"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."}}