{"id":"W3030677385","doi":"","title":"Fragmentation and Forward Error Correction for LoRaWAN small MTU networks","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Semtech (Canada)","funders":"","keywords":"Payload (computing); Fragmentation (computing); Network packet; Computer science; Computer network; Bit error rate; Error detection and correction; Forward error correction; Sensitivity (control systems); Channel (broadcasting); Real-time computing; Telecommunications; Electronic engineering; Decoding methods; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001591553,0.000320976,0.0003415214,0.00008936537,0.0002259042,0.0003613859,0.000399405,0.0003536703,0.00003390451],"category_scores_gemma":[0.0002279215,0.000371646,0.0001501512,0.0001895239,0.00006929831,0.00009227997,0.0003411414,0.0005884311,0.000006516571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024263,"about_ca_system_score_gemma":0.00005775554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001888689,"about_ca_topic_score_gemma":0.000939184,"domain_scores_codex":[0.997867,0.0007406073,0.0004219765,0.0005088016,0.0001522067,0.0003094337],"domain_scores_gemma":[0.9976983,0.0007027952,0.0002062017,0.0006757845,0.0005503806,0.0001665603],"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":[0.0001591599,0.0003035868,0.002416214,0.002497416,0.0005332518,0.000006228927,0.01443205,0.2558022,0.001309716,0.01700566,0.04439843,0.661136],"study_design_scores_gemma":[0.000533939,0.000001035727,0.0008793241,0.001035788,0.00005723626,0.000003432244,0.00006023808,0.9683356,0.004135698,0.002758967,0.02178839,0.0004103043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008424109,0.0007802956,0.9768927,0.001778723,0.001030767,0.004338599,0.00004725548,0.0005120285,0.006195495],"genre_scores_gemma":[0.9147428,0.0009495373,0.07357322,0.0002249478,0.0003440249,0.005447342,0.002273617,0.0002072537,0.002237293],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9063187,"threshold_uncertainty_score":0.9998735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929603416446862,"score_gpt":0.2344050915433306,"score_spread":0.215109057378862,"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."}}