{"id":"W2084167732","doi":"10.1139/x05-046","title":"NIR spectral information used to predict water content of pine seeds from multivariate calibration","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vetenskapsrådet","keywords":"Calibration; Multivariate statistics; Water content; Partial least squares regression; Content (measure theory); Spectral line; Chemistry; Degree (music); Canopy; Biological system; Mathematics; Botany; Analytical Chemistry (journal); Horticulture; Environmental chemistry; Statistics; Biology; Physics; Geology","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":[0.0002463397,0.0004686318,0.0001916813,0.0004243072,0.0001587648,0.0002146062,0.0001731096,0.0001886641,0.000524956],"category_scores_gemma":[0.0007376032,0.0001935393,0.0003590155,0.0003937242,0.0001335534,0.000293046,0.0001342261,0.00029683,0.0002415697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003416808,"about_ca_system_score_gemma":0.0002219179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009117633,"about_ca_topic_score_gemma":0.01043591,"domain_scores_codex":[0.9999108,0.00001586666,0.000003573254,0.00002687683,0.00003233517,0.00001054293],"domain_scores_gemma":[0.9998507,0.00005879478,0.00003447018,0.00001496273,0.00003142535,0.000009577976],"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":[0.0005579176,0.0001639186,0.03998716,0.0001347805,0.00009456378,0.0001136883,0.0001622702,0.4079039,0.4325877,0.0005965651,0.0006537284,0.117044],"study_design_scores_gemma":[0.00001010529,0.00009554994,0.05534406,0.00000753793,0.00002634433,0.00005245411,0.00003915973,0.8345373,0.1087805,0.0005168121,0.0005492796,0.00004088684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9119244,0.0001399015,0.08531435,0.0000357707,0.000009124074,0.00001916941,0.0005558197,0.0008321956,0.001169325],"genre_scores_gemma":[0.9863839,0.00005241952,0.01277991,0.000005419437,0.00000180235,0.00001294178,0.0004256419,0.00002816313,0.0003099808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009117633,"threshold_uncertainty_score":0.01812911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06731719712950365,"score_gpt":0.3195379502120823,"score_spread":0.2522207530825786,"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."}}