{"id":"W2094396019","doi":"10.1016/j.palaeo.2012.01.011","title":"Using paleolimnology to track Holocene climate fluctuations and aquatic ontogeny in poorly buffered High Arctic lakes","year":2012,"lang":"en","type":"article","venue":"Palaeogeography Palaeoclimatology Palaeoecology","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Umeå Universitet","keywords":"Diatom; Holocene; Oceanography; Fragilaria; Paleolimnology; Holocene climatic optimum; Arctic; Geology; Climate change; Biogenic silica; Deglaciation; Ecology; Environmental science; Physical geography; Phytoplankton; Biology; Nutrient; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003953166,0.0001687718,0.0001207033,0.0008482004,0.0005450236,0.0007690305,0.0001774485,0.0001757121,0.0003651104],"category_scores_gemma":[0.001041397,0.0001510251,0.0001192376,0.0005951463,0.0002598406,0.0003838064,0.0003773759,0.0001162266,0.00006920203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006375469,"about_ca_system_score_gemma":0.000506691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.111415,"about_ca_topic_score_gemma":0.3347956,"domain_scores_codex":[0.9999231,0.00002560111,0.000007042213,0.00001970464,0.0000090123,0.00001544565],"domain_scores_gemma":[0.999697,0.00008621245,0.00008720825,0.00001383808,0.00006192642,0.00005377808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006701456,0.00001432639,0.994741,0.00000564229,0.00004127845,0.0000184035,0.0004055248,0.0003466989,0.001239376,0.00001923445,0.00002406189,0.003077352],"study_design_scores_gemma":[0.000001554644,0.00001629603,0.9975802,0.000004950184,0.00001570879,0.00001874642,0.0005560339,0.001439705,0.0002295772,0.00002320999,0.0001113995,0.000002485365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997398,0.00004333662,0.00005185532,0.000004621837,7.042537e-7,0.000001238068,0.00003826056,8.987626e-7,0.0001192502],"genre_scores_gemma":[0.9995407,0.00004899638,0.0001828472,0.000004845598,0.000001188863,0.000002482236,0.00009132358,0.000001185756,0.0001263846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.111415,"threshold_uncertainty_score":0.2215331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02434141883484958,"score_gpt":0.2686809793513679,"score_spread":0.2443395605165183,"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."}}