{"id":"W2418633560","doi":"","title":"Selecting Dry Fiber Weight For Higher and Better Quality Jack Pine Fiber Production","year":2007,"lang":"en","type":"article","venue":"Wood and Fiber Science (Society of Wood Science and Technology)","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Forest Service; U.S. Forest Service; FPInnovations","keywords":"Heritability; Volume (thermodynamics); Fiber; Selection (genetic algorithm); Animal science; Genetic correlation; Dry weight; Genetic gain; Genetic variation; Biology; Botany; Composite material; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0003894015,0.0003647721,0.000212664,0.000438417,0.0001940286,0.0002224887,0.0002413239,0.0001348344,0.0007601699],"category_scores_gemma":[0.0002363714,0.0001378205,0.0001188487,0.0001790729,0.0001545074,0.0001195051,0.0002030208,0.0001565345,0.0001431138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000283475,"about_ca_system_score_gemma":0.0002828856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005886686,"about_ca_topic_score_gemma":0.0241606,"domain_scores_codex":[0.9998449,0.00004004189,0.00001149355,0.00004018895,0.00003302185,0.00003038781],"domain_scores_gemma":[0.9997568,0.00003705766,0.00005995094,0.00001521004,0.00004024589,0.00009073636],"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":[0.0006940415,0.0004531858,0.1174875,0.00003864974,0.00005665944,0.0004801192,0.0001947606,0.0004662114,0.8639725,0.000102915,0.0001227587,0.01593062],"study_design_scores_gemma":[0.0001269741,0.001667023,0.9361504,0.00001101472,0.0001078187,0.001178744,0.000353861,0.003190566,0.05472572,0.00008769829,0.002369258,0.00003099261],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995048,0.00001900762,0.0002756041,0.000004868435,0.000001345787,0.000007312779,0.0000187709,0.000005641132,0.0001626463],"genre_scores_gemma":[0.995246,0.00004812336,0.003161677,0.00003051663,0.000003271296,0.00001554012,0.0002118416,0.00001265736,0.001270397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005886686,"threshold_uncertainty_score":0.0117048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01524476996302595,"score_gpt":0.2434469205022073,"score_spread":0.2282021505391813,"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."}}