{"id":"W4293197931","doi":"","title":"Water activity in seed and pollen banks: an efficient tool to improve conservation of forest genetic resources","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts (Québec)","funders":"","keywords":"Pollen; Water conservation; Genetic algorithm; Water resources; Computer science; Agroforestry; Environmental science; Ecology; Biology; Machine learning","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":[0.0003979901,0.0003142282,0.0003540615,0.001032974,0.0002216331,0.0005328643,0.0004807438,0.0003542357,0.001651779],"category_scores_gemma":[0.0003176469,0.0001507746,0.0001454696,0.0009462778,0.0002330134,0.0007217426,0.0003242228,0.0003375174,0.0004394046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003165127,"about_ca_system_score_gemma":0.0002857791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002082101,"about_ca_topic_score_gemma":0.004275703,"domain_scores_codex":[0.9998176,0.0000416102,0.000008031579,0.00005719651,0.00006453213,0.00001117785],"domain_scores_gemma":[0.9997813,0.00004105129,0.00007831807,0.00001953073,0.00004934797,0.00003052197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003736837,0.0002173253,0.09250929,0.0008260466,0.0001001855,0.0001937573,0.0002316818,0.003285317,0.4185159,0.001385424,0.003706998,0.4786545],"study_design_scores_gemma":[0.00007890665,0.001386532,0.6487579,0.0004032843,0.0003866722,0.0007235892,0.0007022841,0.05868135,0.220883,0.005190775,0.06259408,0.0002115054],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8379154,0.02413658,0.1162559,0.001542417,0.0002158675,0.0002111877,0.004515762,0.001533377,0.01367358],"genre_scores_gemma":[0.927518,0.006043186,0.05972042,0.000160463,0.0001070019,0.0001228264,0.001463968,0.0001251136,0.004738938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002082101,"threshold_uncertainty_score":0.005525827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006821821967122262,"score_gpt":0.189694688704088,"score_spread":0.1828728667369657,"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."}}