{"id":"W2122562057","doi":"10.1002/cem.1038","title":"Standardization of line‐scan NIR imaging systems","year":2007,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"ProSensus (Canada); McMaster University","funders":"","keywords":"Standardization; Pixel; Principal component analysis; Detector; Computer science; Sensitivity (control systems); Line (geometry); Homogeneous; Artificial intelligence; Pattern recognition (psychology); Mathematics; Electronic engineering; Engineering; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.001207074,0.0001520508,0.0005114651,0.001778501,0.0000452289,0.00004721792,0.0003102436,0.0001097651,0.0002091486],"category_scores_gemma":[0.001426228,0.0001332757,0.0002195284,0.004284419,0.00005663804,0.0001975258,0.0000348161,0.0003149705,0.000001825785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003113158,"about_ca_system_score_gemma":0.0001213075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001114081,"about_ca_topic_score_gemma":5.559136e-7,"domain_scores_codex":[0.9976794,0.000009017576,0.001043908,0.0001221926,0.0008751924,0.0002703148],"domain_scores_gemma":[0.9969926,0.0003023789,0.001329547,0.0002098456,0.001006942,0.0001586893],"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":[0.0005079582,0.0009310715,0.2289602,0.001471094,0.001055519,0.0002346433,0.0005347948,0.003225022,0.7207713,0.0005906813,0.008164032,0.03355364],"study_design_scores_gemma":[0.00087673,0.00006804217,0.0007922438,0.00008132692,0.0002949453,0.0001784798,0.001235175,0.0003717697,0.9864089,0.00009175346,0.00942032,0.000180343],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4866493,0.01847862,0.4620918,0.0001780875,0.0005879998,0.00005528113,0.00002888801,0.00003891336,0.03189112],"genre_scores_gemma":[0.9965286,0.0003302708,0.00193548,0.00002365732,0.0005080191,2.397206e-7,0.000003551236,0.0000229008,0.0006472607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5098793,"threshold_uncertainty_score":0.5434824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254522703128571,"score_gpt":0.2915245898714079,"score_spread":0.2789793628401223,"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."}}