{"id":"W4415819655","doi":"10.3390/ijgi14110434","title":"Multimodal Spatiotemporal Deep Fusion for Highway Traffic Accident Prediction in Toronto: A Case Study and Roadmap","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensor fusion; Intelligent transportation system; Fusion; Crash; Feature (linguistics); Road traffic; Hotspot (geology); Road surface","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003736634,0.0001186489,0.000158092,0.0002979319,0.00006000237,0.00009087231,0.0001352669,0.00008169894,0.00002363142],"category_scores_gemma":[0.00004362733,0.000109139,0.00006450294,0.00007080513,0.00001125289,0.00220374,0.00002766465,0.0001437606,0.000002374126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005198392,"about_ca_system_score_gemma":0.0000456435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002989649,"about_ca_topic_score_gemma":0.001701278,"domain_scores_codex":[0.9987761,0.00002091462,0.0007543657,0.00005830178,0.0002761741,0.0001141545],"domain_scores_gemma":[0.9993666,0.00005523583,0.0001750618,0.00006374369,0.0002925393,0.00004678574],"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":[0.0005669057,0.0002215237,0.01455858,0.00007081898,0.000292266,0.0001001949,0.01104829,0.3116861,0.00003384076,0.0002234703,0.001371186,0.6598268],"study_design_scores_gemma":[0.006317446,0.0002985882,0.1809196,0.0001481079,0.00005678785,0.0006224836,0.01402306,0.7933534,0.00007141675,0.00003809692,0.003971849,0.0001791431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9432019,0.0001579853,0.0533346,0.0001512348,0.002356282,0.0004070116,0.00002276524,0.00005036492,0.0003178466],"genre_scores_gemma":[0.9986612,0.00008018658,0.001029338,0.00003961297,0.0001227214,0.00001649159,0.00003136875,0.000005447283,0.00001366977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6596476,"threshold_uncertainty_score":0.4450559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004112016674653563,"score_gpt":0.2545576688261713,"score_spread":0.2504456521515178,"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."}}