{"id":"W4302085365","doi":"","title":"DYNAWOOD - Dynamics of wood formation and adaptation of forest trees to climate variation","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Variation (astronomy); Adaptation (eye); Tree (set theory); Climate change; Environmental science; Dynamics (music); Atmospheric sciences; Computer science; Agroforestry; Ecology; Geology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002957007,0.00006400108,0.0001668851,0.0003096206,0.0002220856,0.0007550339,0.0002526996,0.0002191382,0.004819408],"category_scores_gemma":[0.001453472,0.0001825334,0.0001902121,0.0003521998,0.0002302761,0.0004666905,0.0003595333,0.0002237148,0.0004780097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004925813,"about_ca_system_score_gemma":0.0003091476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009436316,"about_ca_topic_score_gemma":0.01914392,"domain_scores_codex":[0.9999274,0.00001312703,0.000004392979,0.00003531018,0.00000595623,0.00001378735],"domain_scores_gemma":[0.9994734,0.0001996771,0.00008640255,0.00006061487,0.0000641334,0.0001158463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008361185,0.0001246688,0.78325,0.0001116397,0.0002273119,0.0001542854,0.0006211763,0.1070302,0.02552116,0.03863609,0.005985408,0.0375018],"study_design_scores_gemma":[0.0000193359,0.00005545128,0.808867,0.000009069368,0.00003427454,0.000169661,0.0004167642,0.1669502,0.001887457,0.01832365,0.003237576,0.00002962601],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950859,0.00007907364,0.002335507,0.000118104,0.000006421347,0.000002856955,0.001521683,0.0000359274,0.0008145827],"genre_scores_gemma":[0.9976841,0.00002633725,0.0005232965,0.000005397812,0.000001429409,0.000003954623,0.0005632446,0.00000965705,0.001182602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009436316,"threshold_uncertainty_score":0.01876277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496083980841262,"score_gpt":0.2161081320229392,"score_spread":0.2011472922145265,"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."}}