{"id":"W1971670466","doi":"10.1080/17480272.2010.493222","title":"Lumber Quality Model: The theory","year":2010,"lang":"en","type":"article","venue":"Wood Material Science and Engineering","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations","funders":"FPInnovations","keywords":"Shrinkage; Kiln; Distortion (music); Calibration; Monte Carlo method; Experimental data; Computer science; Water content; Quality (philosophy); Simulation; Engineering; Mathematics; Statistics; Machine learning; Geotechnical engineering; Waste management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005195232,0.00009608851,0.00007455174,0.00003176467,0.0001324313,0.0001615925,0.0001615823,0.00003240248,0.00003148837],"category_scores_gemma":[0.00002971306,0.00006065267,0.00001317952,0.0001171423,0.000121624,0.0002421771,0.00004496783,0.00008927844,0.000009281437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001173727,"about_ca_system_score_gemma":0.00001379533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001392745,"about_ca_topic_score_gemma":0.000007374799,"domain_scores_codex":[0.9994314,0.000004033419,0.00008906415,0.0001068068,0.0001428781,0.0002257973],"domain_scores_gemma":[0.9997448,0.00001655922,0.000006674162,0.000155211,0.00001894328,0.00005779769],"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":[0.000004109087,0.000003603559,0.00004479447,0.00002311091,0.000007626387,5.959848e-7,0.0004768219,0.006536513,0.9765543,0.014027,0.00006099319,0.00226058],"study_design_scores_gemma":[0.0004473599,0.00004464669,0.008534792,0.00003261343,0.00004057085,0.00003243309,0.0002322937,0.3273607,0.6502572,0.004412788,0.007780245,0.0008242991],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953582,0.00003987663,0.0002642305,0.00005131729,0.0008733189,0.00006375515,0.000004235812,0.0002181675,0.003126949],"genre_scores_gemma":[0.9992475,0.0000132037,0.0005201627,0.00001817935,0.0001154682,0.00001470445,6.6269e-7,0.00001180749,0.00005830787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.326297,"threshold_uncertainty_score":0.2473344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009677530914849788,"score_gpt":0.2068573497710488,"score_spread":0.197179818856199,"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."}}