{"id":"W3037752957","doi":"","title":"Encoding Neighbor Information into Geographical Embeddings Using Convolutional Neural Networks.","year":2020,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Convolutional neural network; Computer science; Encoding (memory); Artificial intelligence; Pattern recognition (psychology)","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":[0.0003180147,0.0009342195,0.0004858197,0.001022401,0.000231061,0.0007314598,0.0009023203,0.0007893209,0.002694593],"category_scores_gemma":[0.002065889,0.0003768733,0.0006421345,0.001301678,0.0003091587,0.001779043,0.0008292245,0.001308098,0.001420273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007475851,"about_ca_system_score_gemma":0.0005272771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01804585,"about_ca_topic_score_gemma":0.03254901,"domain_scores_codex":[0.9998125,0.00003996414,0.00001061362,0.00007196126,0.00002956827,0.00003537238],"domain_scores_gemma":[0.9995015,0.0002007298,0.00006495746,0.00008765164,0.0001166579,0.0000285537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000424564,0.0003302915,0.01150427,0.0002395851,0.0002661977,0.0001964759,0.0001839033,0.3172772,0.007399654,0.01718581,0.03185089,0.6131412],"study_design_scores_gemma":[0.000009110959,0.00003279815,0.001307231,0.00002713904,0.00003215589,0.00003112914,0.00004265827,0.9834087,0.001521275,0.01146973,0.002106432,0.00001154839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2451937,0.004198392,0.723231,0.002086318,0.0009392713,0.00009126796,0.007484326,0.006245896,0.01052984],"genre_scores_gemma":[0.908824,0.0009786239,0.07348666,0.0002356732,0.0001689981,0.00005886809,0.008200617,0.0001760749,0.007870497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01804585,"threshold_uncertainty_score":0.03588158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08277924284267012,"score_gpt":0.3934570905429045,"score_spread":0.3106778477002344,"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."}}