{"id":"W4252419285","doi":"10.7287/peerj-cs.89v0.1/reviews/1","title":"Peer Review #1 of \"TCP adaptation with network coding and opportunistic data forwarding in multi-hop wireless networks (v0.1)\"","year":2016,"lang":"en","type":"peer-review","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"Computer network; Hop (telecommunications); Computer science; Coding (social sciences); Wireless network; Adaptation (eye); Wireless; Telecommunications; Biology; Mathematics; Statistics; Neuroscience","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00846787,0.001002996,0.001419634,0.004647547,0.00389186,0.005001418,0.003134989,0.00279978,0.09824436],"category_scores_gemma":[0.06271119,0.0005728749,0.0009786185,0.002416951,0.001332875,0.003008322,0.003624242,0.002140685,0.07196374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001837973,"about_ca_system_score_gemma":0.008188928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002855581,"about_ca_topic_score_gemma":0.007177198,"domain_scores_codex":[0.9896694,0.00137212,0.0005812998,0.0006006596,0.007087229,0.0006893127],"domain_scores_gemma":[0.8698443,0.006747508,0.002567562,0.006571259,0.1064714,0.007797969],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006449001,0.00005281064,0.000887729,0.0008729452,0.00002951523,0.0002966913,0.0001087481,0.0001703167,0.001044456,0.001712163,0.8643087,0.1304516],"study_design_scores_gemma":[0.00001737889,0.00004528372,0.001251733,0.0002377445,0.00002217456,0.0002421244,0.00008076115,0.000760861,0.00121132,0.0009230785,0.9951828,0.0000246465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.01500144,0.02358733,0.02912335,0.1144884,0.5905196,0.004761676,0.003050019,0.004580032,0.2148882],"genre_scores_gemma":[0.05038321,0.03166343,0.01457683,0.01355913,0.09790755,0.001298025,0.007870556,0.00322051,0.7795208],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9915321,"threshold_uncertainty_score":0.3286601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2182943503462419,"score_gpt":0.3670617808050549,"score_spread":0.148767430458813,"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."}}