{"id":"W3026203006","doi":"10.1016/j.dib.2020.105753","title":"Dataset and methodology on identification and correlation of secondary carbides with microstructure, wear mechanism, and tool performance for different CERMET grades during high-speed dry finish turning of AISI 304 stainless steel","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced materials and composites","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cermet; Carbide; Machining; Insert (composites); Materials science; Microstructure; Metallurgy; Scanning electron microscope; Tool wear; Tool steel; Composite material; Ceramic","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001581437,0.001105193,0.000858532,0.005223311,0.001218323,0.001753842,0.001770191,0.00166738,0.005022241],"category_scores_gemma":[0.004233963,0.0003677592,0.001566073,0.003142802,0.0004775725,0.001030355,0.001518397,0.0008885522,0.005234991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114069,"about_ca_system_score_gemma":0.002048693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01098598,"about_ca_topic_score_gemma":0.02005496,"domain_scores_codex":[0.9974796,0.0001585529,0.0004198808,0.0009766512,0.0007279987,0.0002373882],"domain_scores_gemma":[0.9982014,0.0003556843,0.0002453963,0.0004581957,0.0006327577,0.0001064953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001348144,0.00135995,0.1479879,0.006109243,0.0005914833,0.003023367,0.0008837247,0.0160993,0.05258597,0.008888336,0.2507166,0.510406],"study_design_scores_gemma":[0.0002654199,0.0008541891,0.2393468,0.00121812,0.0004326849,0.003934416,0.001576075,0.07489847,0.06990907,0.01474673,0.5925086,0.0003094844],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1027987,0.003503323,0.09844337,0.001094533,0.0005789404,0.002246654,0.7657003,0.01262939,0.01300462],"genre_scores_gemma":[0.05859517,0.000848098,0.07124906,0.0002933178,0.00007279986,0.0019545,0.8621339,0.0002409131,0.004612181],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01098598,"threshold_uncertainty_score":0.02184403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993405314471498,"score_gpt":0.2497595650737838,"score_spread":0.2198255119290688,"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."}}