{"id":"W2006488799","doi":"10.3390/ijgi4010001","title":"Measure of Landmark Semantic Salience through Geosocial Data Streams","year":2014,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Landmark; Salience (neuroscience); Computer science; Semantic similarity; Information retrieval; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002193516,0.000684412,0.0007510268,0.009471186,0.0006037721,0.001697785,0.0007895086,0.001107875,0.000963911],"category_scores_gemma":[0.01568417,0.0002123886,0.0004721063,0.008104425,0.0008149713,0.004600814,0.002395232,0.0007251761,0.0004223245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000841843,"about_ca_system_score_gemma":0.0007262639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003653551,"about_ca_topic_score_gemma":0.003243297,"domain_scores_codex":[0.9976465,0.0005671522,0.0002869203,0.0005014556,0.0008408635,0.0001570506],"domain_scores_gemma":[0.9925343,0.003125775,0.001674935,0.0006716824,0.001679136,0.0003141056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002001171,0.0009658531,0.5021336,0.001517632,0.0007363593,0.001044222,0.004330491,0.06337573,0.01423939,0.04388311,0.01183111,0.3539413],"study_design_scores_gemma":[0.00008371194,0.0005766492,0.383807,0.0003064384,0.0004530443,0.001106077,0.008431937,0.4861827,0.01540523,0.07563606,0.02773746,0.0002737644],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7822534,0.001439444,0.1860381,0.001030534,0.0002026846,0.0005300901,0.01759962,0.0008949999,0.01001114],"genre_scores_gemma":[0.952591,0.0003070912,0.03784363,0.00005050201,0.00007510302,0.0002423304,0.008417367,0.00004251584,0.0004305105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009471186,"threshold_uncertainty_score":0.01160055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241250688168402,"score_gpt":0.2508237643511547,"score_spread":0.2384112574694707,"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."}}