{"id":"W4404407032","doi":"10.48550/arxiv.2411.08171","title":"Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Transfer of learning; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004996981,0.001769812,0.0008844694,0.001852408,0.0004991638,0.001067478,0.001808318,0.001503433,0.001412778],"category_scores_gemma":[0.008616183,0.000370229,0.0008984738,0.001221039,0.0007390495,0.003226972,0.001025877,0.00169974,0.0006674973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002661586,"about_ca_system_score_gemma":0.001039579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0147134,"about_ca_topic_score_gemma":0.01585136,"domain_scores_codex":[0.9988008,0.0003547633,0.00008293925,0.0002908906,0.0003214582,0.0001491622],"domain_scores_gemma":[0.996786,0.001833395,0.0001960869,0.0004814727,0.0005882476,0.0001148353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008853011,0.0006822291,0.01805245,0.0003613782,0.0004802739,0.0001640918,0.0001364909,0.6407141,0.003732842,0.00311555,0.007257237,0.3244181],"study_design_scores_gemma":[0.00001755565,0.0002798877,0.002885712,0.0000299195,0.00004030922,0.0000436324,0.00004937603,0.990497,0.003905932,0.001485729,0.0007465839,0.00001842331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8607585,0.005667031,0.1138504,0.001210193,0.0004433801,0.0002727625,0.001369373,0.005782067,0.01064623],"genre_scores_gemma":[0.9637352,0.0008717504,0.03000988,0.0001670924,0.00005622301,0.00006586156,0.002323638,0.0001891878,0.002581243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0147134,"threshold_uncertainty_score":0.02925551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09054915274491343,"score_gpt":0.2059572141948824,"score_spread":0.115408061449969,"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."}}