{"id":"W7138388437","doi":"10.18653/v1/2025.findings-ijcnlp.53","title":"WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada","year":2025,"lang":"","type":"article","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Content (measure theory); Feature (linguistics); Vegetation (pathology); Quality (philosophy)","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":[0.0005135263,0.001642876,0.000837728,0.003634488,0.00149125,0.001161314,0.001673055,0.001670539,0.003009124],"category_scores_gemma":[0.001622882,0.0003173148,0.0009113859,0.003587797,0.0006663717,0.0009221169,0.001409772,0.00109463,0.003128939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004473119,"about_ca_system_score_gemma":0.005120169,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8192994,"about_ca_topic_score_gemma":0.9290484,"domain_scores_codex":[0.999345,0.00005892065,0.00004209379,0.0001628812,0.0002255177,0.0001656628],"domain_scores_gemma":[0.9989477,0.0001347054,0.00004954934,0.0001322641,0.0005461733,0.0001897136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001726761,0.0007837889,0.0850919,0.001972655,0.0007405994,0.001775768,0.00266149,0.005592037,0.01632228,0.0007919517,0.6883751,0.1941658],"study_design_scores_gemma":[0.0002780553,0.0003720419,0.6311172,0.0008369914,0.0004656249,0.001503638,0.01115457,0.04518935,0.01575124,0.001119186,0.2916797,0.0005324738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2588071,0.004215254,0.003667956,0.0009634325,0.0003692248,0.0004359242,0.7187554,0.005620384,0.007165371],"genre_scores_gemma":[0.2062426,0.001176461,0.00850441,0.0002507354,0.00007493228,0.0003119747,0.7753137,0.000314902,0.007810205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1807006,"threshold_uncertainty_score":0.3635295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01989857163845972,"score_gpt":0.2340337664158274,"score_spread":0.2141351947773677,"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."}}