{"id":"W2053100629","doi":"10.1111/j.1365-2699.2008.02021.x","title":"A new methodology for reconstructing climate and vegetation from modern pollen assemblages: an example from British Columbia","year":2008,"lang":"en","type":"article","venue":"Journal of Biogeography","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Victoria; Parks Canada; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Directorate for Biological Sciences; Simon Fraser University; Parks Canada","keywords":"Ordination; Pollen; Multivariate statistics; Partial least squares regression; Physical geography; Geography; Vegetation (pathology); Ecology; Statistics; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005378085,0.0002930941,0.0001647781,0.001090982,0.001173936,0.0008810473,0.0004879513,0.0002476049,0.00100404],"category_scores_gemma":[0.001326845,0.0001803543,0.0002336873,0.002173543,0.0003792625,0.0002561725,0.0004257786,0.0003420739,0.0001315264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002385907,"about_ca_system_score_gemma":0.002860058,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7798017,"about_ca_topic_score_gemma":0.886328,"domain_scores_codex":[0.9998307,0.0000489693,0.00001174381,0.00004011272,0.00004935191,0.00001899283],"domain_scores_gemma":[0.9995874,0.0001280653,0.00002704996,0.00004738948,0.0001787997,0.00003140928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001354655,0.00004537163,0.2798707,0.0002422916,0.0001928724,0.001791038,0.00241252,0.1078472,0.01474051,0.007003197,0.005412851,0.580306],"study_design_scores_gemma":[0.0000594945,0.00003747761,0.3777767,0.0001074883,0.00008876988,0.001091441,0.001527798,0.5689765,0.005356589,0.004542962,0.04030524,0.0001296609],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8280527,0.0008858723,0.1531153,0.0004984475,0.00003186256,0.00009888616,0.001281323,0.0007593722,0.0152762],"genre_scores_gemma":[0.7728294,0.0003578635,0.2220827,0.00005520686,0.000009077225,0.0000480631,0.0007123491,0.0001269416,0.003778385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2201983,"threshold_uncertainty_score":0.4429901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08419805443931762,"score_gpt":0.2744707676320387,"score_spread":0.1902727131927211,"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."}}