{"id":"W1988614095","doi":"10.1046/j.1529-8817.2000.00049.x","title":"TRACKING LONG‐TERM CHANGES IN CLIMATE USING ALGAL INDICATORS IN LAKE SEDIMENTS","year":2000,"lang":"en","type":"article","venue":"Journal of Phycology","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":321,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Climate change; Proxy (statistics); Environmental science; Physical geography; Ecology; Paleoclimatology; Hindcast; Arctic; Climatology; Biology; Geology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001068047,0.00009810824,0.000321123,0.0008949654,0.00006479458,0.00001137971,0.0002311761,0.0001999928,0.01886932],"category_scores_gemma":[0.00003375902,0.00008306826,0.000042353,0.0004415371,0.0001523542,0.0001561262,0.000009860731,0.0005548432,0.00007024041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004461173,"about_ca_system_score_gemma":0.00008602179,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000330867,"about_ca_topic_score_gemma":0.03986395,"domain_scores_codex":[0.9984535,0.0003433073,0.0003699583,0.0001336035,0.0001488901,0.0005507223],"domain_scores_gemma":[0.9994466,0.0002016982,0.0001633606,0.00006811789,0.00002077179,0.00009947524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002154567,0.00005043926,0.9817784,0.00001436862,0.00001507916,0.001105498,0.0003149564,0.0007496842,0.00002671278,0.000002589297,0.000007193619,0.01571965],"study_design_scores_gemma":[0.0008348629,0.0002731851,0.9972151,0.00003715603,0.000008765142,0.0008307412,0.00004803707,0.0002984421,0.00005822887,0.00009599703,0.0002262953,0.00007321891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966019,0.0004971567,9.719366e-7,0.0003021234,0.0002680418,0.0000765609,0.000007855587,0.000003050393,0.002242327],"genre_scores_gemma":[0.9991617,0.0003888801,0.0001094597,0.0002177482,0.00008826658,3.402425e-7,0.000007501433,0.000002318687,0.00002374681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03983087,"threshold_uncertainty_score":0.9820276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02829667631151547,"score_gpt":0.2961302774120791,"score_spread":0.2678336011005636,"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."}}