{"id":"W7018393018","doi":"","title":"Development of new signal conditioner for data acquisition","year":2007,"lang":"en","type":"report","venue":"NPARC","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Signal conditioning; Data acquisition; SIGNAL (programming language); Channel (broadcasting); Signal processing; Continuation; Selection (genetic algorithm); Emphasis (telecommunications)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006791545,0.0005401315,0.0006812617,0.001102337,0.0005291826,0.0008485204,0.001033173,0.0009277902,0.01182484],"category_scores_gemma":[0.0008393351,0.0004676094,0.0004032766,0.0004805223,0.0002971477,0.001756588,0.0005693386,0.0009426016,0.004900225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005725316,"about_ca_system_score_gemma":0.001034109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006124904,"about_ca_topic_score_gemma":0.000972355,"domain_scores_codex":[0.998968,0.00005080204,0.00005437762,0.000175414,0.0006709333,0.00008042486],"domain_scores_gemma":[0.9990633,0.0001273637,0.00005981493,0.0001211343,0.0005685131,0.00005987828],"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":[0.0001445726,0.000144984,0.0009011137,0.0006181029,0.0000220533,0.0002792867,0.0001837739,0.001678563,0.674243,0.005332032,0.01389844,0.3025541],"study_design_scores_gemma":[0.0000548729,0.0006966899,0.003349856,0.00006711432,0.00003901799,0.00204248,0.00005646674,0.01588064,0.7035481,0.0008974236,0.2733048,0.00006260726],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05378691,0.002455612,0.8827756,0.0007129029,0.0008127074,0.002190838,0.001220878,0.01539854,0.040646],"genre_scores_gemma":[0.1181114,0.002925566,0.7848788,0.0006242143,0.0003305638,0.001431345,0.004469933,0.0009936619,0.08623455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01182484,"threshold_uncertainty_score":0.03955805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1782013757187337,"score_gpt":0.385773814787884,"score_spread":0.2075724390691503,"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."}}