{"id":"W4392134148","doi":"10.1016/b978-0-323-99931-1.00171-9","title":"Using DNA archived in lake sediments to reconstruct past ecosystems","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; McGill University","funders":"","keywords":"Lake ecosystem; Workflow; Environmental change; Ecosystem; Environmental DNA; Earth science; Paleolimnology; Sedimentary rock; Environmental science; Geology; Environmental resource management; Physical geography; Ecology; Geography; Paleontology; Climate change; Oceanography; Computer science; Biodiversity; Biology; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155265,0.0002921305,0.0001767542,0.002862487,0.0002504856,0.001982789,0.0006790662,0.0007200405,0.006720146],"category_scores_gemma":[0.001130413,0.000308548,0.0002673021,0.003255,0.0007101602,0.001248166,0.0005276109,0.0005641502,0.003318522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005527302,"about_ca_system_score_gemma":0.0004770801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004438135,"about_ca_topic_score_gemma":0.01555978,"domain_scores_codex":[0.9998055,0.00003856431,0.00001794509,0.0000537302,0.00007784279,0.000006414073],"domain_scores_gemma":[0.9994513,0.0003727551,0.00004538921,0.00004928212,0.00006022736,0.00002094236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006304252,0.00003378628,0.01143983,0.0008911485,0.00007783799,0.0005172325,0.001585305,0.00268681,0.09428028,0.01311773,0.009272742,0.8660342],"study_design_scores_gemma":[0.0000232975,0.0001154324,0.0554742,0.00152839,0.000172573,0.001961501,0.001402297,0.00826687,0.1247825,0.03573031,0.7704352,0.0001075979],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1344912,0.04913326,0.5885,0.002379762,0.001290122,0.0002339018,0.02469837,0.00236844,0.1969048],"genre_scores_gemma":[0.1166956,0.03947807,0.6754344,0.0006974126,0.0002680046,0.0001901665,0.01311684,0.000896982,0.1532227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006720146,"threshold_uncertainty_score":0.02248114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377687751344237,"score_gpt":0.2262365169263664,"score_spread":0.202459639412924,"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."}}