{"id":"W4280527838","doi":"10.1002/ecs2.4089","title":"Seedlot Selection Tool and Climate‐Smart Restoration Tool: Web‐based tools for sourcing seed adapted to future climates","year":2022,"lang":"en","type":"article","venue":"Ecosphere","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Forest Service; U.S. Forest Service","keywords":"Climate change; Selection (genetic algorithm); Environmental science; Environmental resource management; Ecology; Biology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003983625,0.0001472389,0.0001380073,0.0000220421,0.0006666916,0.0001970829,0.0001172511,0.00004006165,0.001986312],"category_scores_gemma":[0.00002232991,0.0001460475,0.00004892565,0.0002538698,0.00001385533,0.0003496475,0.000173034,0.0001034242,0.0001202625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001990382,"about_ca_system_score_gemma":0.00001333009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003859691,"about_ca_topic_score_gemma":0.0003407396,"domain_scores_codex":[0.9988329,0.00005629229,0.0002028689,0.0003572873,0.0002411735,0.0003095151],"domain_scores_gemma":[0.9996578,0.00005188155,0.00007424705,0.000147195,0.000008316481,0.00006055268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001015585,0.0003780051,0.176005,0.0001918919,0.00005850404,0.00001069803,0.001115675,0.3159739,0.01812617,0.001419764,0.4323981,0.05330665],"study_design_scores_gemma":[0.00116124,0.0004708378,0.1834957,0.00001400313,0.00003455609,0.000004067866,0.001225221,0.07336002,0.0002425713,0.00007649728,0.7395418,0.0003734507],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904346,0.00003359998,0.001417309,0.002025294,0.0002817879,0.0008688948,0.00005142271,0.0001298293,0.004757271],"genre_scores_gemma":[0.9933076,0.00001041392,0.003859734,0.001642609,0.0001675761,0.0003455699,0.00008076193,0.00002736951,0.0005583904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3071437,"threshold_uncertainty_score":0.998926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027166728627562,"score_gpt":0.2113983905062724,"score_spread":0.2011267232199968,"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."}}