{"id":"W2162400456","doi":"10.1139/x07-122","title":"Boreal forest provenance tests used to predict optimal growth and response to climate change. 1. Jack pine","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Climate change; Taiga; Latitude; Environmental science; Boreal; Precipitation; Limiting; Population; Physical geography; Atmospheric sciences; Climatology; Ecology; Geography; Forestry; Meteorology; Biology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002142419,0.0001628539,0.0002342278,0.0005858401,0.0005606092,0.00005995477,0.0005882443,0.00009482858,0.0001595073],"category_scores_gemma":[0.0009649721,0.0001450234,0.00005030935,0.0007444773,0.0006066257,0.0003549433,0.000255395,0.0004347562,0.0002185631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004762121,"about_ca_system_score_gemma":0.000364199,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01096165,"about_ca_topic_score_gemma":0.3131409,"domain_scores_codex":[0.997421,0.000212577,0.0003322705,0.0003173028,0.0005837712,0.001133077],"domain_scores_gemma":[0.9974859,0.0001715123,0.00007908452,0.000273882,0.00009478135,0.00189483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006818928,0.00003927542,0.9693118,0.0000163847,0.00001371801,0.001621,0.00159291,0.000652284,0.0001402316,0.001151955,0.02417378,0.0006048188],"study_design_scores_gemma":[0.0005681045,0.00208786,0.9829226,0.00006751969,0.000005687862,0.0002570675,0.00009520438,0.00007527681,0.00006797769,0.0004939034,0.01321258,0.0001462654],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882116,0.00005694247,0.00007086866,0.008194824,0.0001254444,0.0007819491,0.00002557139,0.000007660406,0.002525094],"genre_scores_gemma":[0.9978395,0.00006525055,0.001022196,0.0004216591,0.0001441744,0.00006838862,0.000002538025,0.00002473765,0.0004115558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3021793,"threshold_uncertainty_score":0.9956244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04198562405435843,"score_gpt":0.2923134443485349,"score_spread":0.2503278202941765,"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."}}