{"id":"W2112312184","doi":"10.1139/er-2012-0041","title":"Establishing past environmental conditions and tracking long-term environmental change in the Canadian Maritime provinces using lake sediments","year":2013,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Environmental change; Environmental science; Climate change; Ecology; Paleolimnology; Water quality; Nova scotia; Watershed; Eutrophication; Environmental monitoring; Geography; Nutrient; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006377265,0.0004244128,0.0003728013,0.0000870911,0.0007983916,0.0001775356,0.0004616714,0.0001997563,0.02309328],"category_scores_gemma":[0.00001148418,0.0003422767,0.0001037319,0.0001043488,0.0008333119,0.001571754,0.0002606002,0.0004494551,0.004219912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009887466,"about_ca_system_score_gemma":0.00001169324,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009431856,"about_ca_topic_score_gemma":0.08716436,"domain_scores_codex":[0.9971814,0.0003865874,0.0005786268,0.0006528095,0.0004335316,0.0007670738],"domain_scores_gemma":[0.9990216,0.00006321326,0.0002192464,0.0003959521,1.711622e-7,0.0002997463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001975706,0.0002545314,0.9734755,0.000007596487,0.000007923182,0.00004889606,0.001150312,0.000002540162,0.006139317,0.00000157345,0.0003302904,0.01857954],"study_design_scores_gemma":[0.0003735528,0.00006361364,0.9932377,0.00005265735,0.00006533378,0.0001150555,0.0003827654,0.0004024769,0.0001107317,0.00002983431,0.004765139,0.0004011293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952382,0.001229546,0.000005405376,0.0002961311,0.0001307591,0.002568086,0.00008711832,0.00001207495,0.0004326179],"genre_scores_gemma":[0.9956915,0.0007354352,0.0002097158,0.002148609,0.0001035806,0.0005568037,0.0002976989,0.00003637108,0.0002203373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0777325,"threshold_uncertainty_score":0.9999029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03517309926218513,"score_gpt":0.2583747731467874,"score_spread":0.2232016738846023,"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."}}