{"id":"W2608163295","doi":"10.1039/c6gc03581g","title":"Tracking and predicting wood fibers processing with fluorescent carbohydrate binding modules","year":2017,"lang":"en","type":"article","venue":"Green Chemistry","topic":"Advanced Cellulose Research Studies","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fluorescence; Polysaccharide; Carbohydrate; Fiber; Fluorescent labelling; Chemistry; Tracking (education); Biological system; Nanotechnology; Computer science; Materials science; Biochemistry; Organic chemistry; Optics; Biology","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":[0.0001458476,0.0004397411,0.0001889395,0.0003686155,0.0001161412,0.0004424074,0.0001642742,0.0005102676,0.0006426956],"category_scores_gemma":[0.0002752356,0.0001893253,0.000302896,0.0003106709,0.0001127543,0.0004877446,0.0001133447,0.0003407154,0.0004255895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003128397,"about_ca_system_score_gemma":0.0001983619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001821721,"about_ca_topic_score_gemma":0.003401342,"domain_scores_codex":[0.999951,0.000005042857,0.000001678428,0.0000178528,0.00001432472,0.00001011477],"domain_scores_gemma":[0.9999247,0.00002312558,0.00002394707,0.000005737837,0.00001385133,0.000008672557],"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":[0.00009243012,0.00005229718,0.006601013,0.00006822107,0.00001746846,0.0000390555,0.00001771086,0.02058,0.9540603,0.0003694366,0.0001247578,0.01797723],"study_design_scores_gemma":[0.000007360687,0.0001773218,0.01025934,0.000008518919,0.00003114641,0.00006134131,0.00003689758,0.3116707,0.676104,0.0005689121,0.001058348,0.00001622348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9162616,0.0004766973,0.08153123,0.00003160119,0.000008217024,0.00002833439,0.0003702052,0.0002496497,0.001042449],"genre_scores_gemma":[0.9168105,0.0008049559,0.07968044,0.0000208051,0.000004424398,0.00003456785,0.0007033409,0.00005275816,0.001888206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001821721,"threshold_uncertainty_score":0.003622174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220709131972687,"score_gpt":0.2744033358387084,"score_spread":0.2523324226414397,"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."}}