{"id":"W61121905","doi":"","title":"Experiments for Fire Detection Using a Wireless Sensor Network","year":2012,"lang":"en","type":"article","venue":"International Conference on Sensor Technologies and Applications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Fire detection; Wireless sensor network; Context (archaeology); Computer science; Set (abstract data type); Wireless; Real-time computing; Wind speed; Computer network; Telecommunications; Meteorology; Engineering; Geology; Geography; Architectural engineering","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":[0.001009896,0.0008059238,0.0004407919,0.0005550287,0.0003852826,0.0003345965,0.0009212334,0.0007802359,0.001345642],"category_scores_gemma":[0.002291104,0.0002272797,0.0005004179,0.0005051522,0.0004291211,0.000685168,0.0003816241,0.0006813039,0.0002561063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949262,"about_ca_system_score_gemma":0.0002140952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005884607,"about_ca_topic_score_gemma":0.000600782,"domain_scores_codex":[0.9990125,0.0002498666,0.00009881562,0.0002079001,0.0003017207,0.0001291291],"domain_scores_gemma":[0.9971495,0.001513876,0.0003455186,0.0003812207,0.0004496646,0.0001601032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004420111,0.005199928,0.01957665,0.001102674,0.0003474694,0.0009452471,0.0005054807,0.1406377,0.7681131,0.001834315,0.001466271,0.05585103],"study_design_scores_gemma":[0.0003308233,0.01364481,0.0208081,0.0000676659,0.0001731317,0.0008129629,0.0004866361,0.2805344,0.6774004,0.001614463,0.004029885,0.0000967498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731108,0.0001694593,0.02434894,0.00009951588,0.0001040028,0.0002277915,0.0004848109,0.0003591831,0.001095427],"genre_scores_gemma":[0.9740956,0.0002300444,0.02380861,0.00005302756,0.00001507984,0.0002552652,0.0005311573,0.00003748724,0.0009736353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001345642,"threshold_uncertainty_score":0.005340934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05721113280486274,"score_gpt":0.3108915802166299,"score_spread":0.2536804474117672,"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."}}