{"id":"W6998768373","doi":"","title":"Aquatic biodiversity patterns along gradients of multiple stressors and disturbance histories: Integration of paleocology and molecular techniques","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Disturbance (geology); Biodiversity; Stressor; Aquatic environment; Ecosystem","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034738,0.0001546827,0.0002363579,0.0009322286,0.0004239142,0.0009050941,0.0002684268,0.0002981724,0.001045797],"category_scores_gemma":[0.0004369337,0.0002061414,0.000180506,0.001539747,0.0004082333,0.0004879199,0.0006056608,0.0004187891,0.0002085899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003227591,"about_ca_system_score_gemma":0.00026987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01281589,"about_ca_topic_score_gemma":0.0322402,"domain_scores_codex":[0.9998717,0.0000247704,0.000005849224,0.00004546082,0.00002464525,0.00002755398],"domain_scores_gemma":[0.9997222,0.00006228139,0.0000941443,0.00001288205,0.00006639343,0.00004213728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000259787,0.00007291942,0.8765299,0.00008101467,0.0001495259,0.00006747559,0.001853312,0.0003726484,0.0610248,0.0002180512,0.0003741713,0.05899628],"study_design_scores_gemma":[0.00000131456,0.00001927288,0.9986593,0.000007041956,0.00001081784,0.00002453299,0.0003650843,0.0001539406,0.0004658886,0.00005374219,0.0002350161,0.000004075661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971096,0.0003676103,0.0009216805,0.00005252694,0.000004466442,0.000009810353,0.0004041038,0.00000674758,0.001123331],"genre_scores_gemma":[0.9951925,0.0005421506,0.002775546,0.00009109965,0.00000959353,0.00003309598,0.0004332398,0.00001167116,0.0009111785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01281589,"threshold_uncertainty_score":0.02548259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345230316177581,"score_gpt":0.2258229334064162,"score_spread":0.2123706302446404,"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."}}