{"id":"W2572201401","doi":"10.3390/d9010005","title":"Tracking the Recovery of Freshwater Mussel Diversity in Ontario Rivers: Evaluation of a Quadrat-Based Monitoring Protocol","year":2017,"lang":"en","type":"article","venue":"Diversity","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Trent University; Ministry of Natural Resources and Forestry","funders":"Fisheries and Oceans Canada; Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Quadrat; Mussel; Sampling (signal processing); Abundance (ecology); Ecology; Protocol (science); Environmental science; Environmental DNA; Population; Watershed; Biodiversity; Fishery; Biology; Computer science; Transect; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001105073,0.00007834914,0.0001277798,0.00002713585,0.0008796446,0.00001121238,0.0005114046,0.00007948162,0.003595528],"category_scores_gemma":[0.00008583086,0.00006287299,0.00006293768,0.00003882336,0.0003121308,0.0004026972,0.001136701,0.0001410305,0.00005249663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004476737,"about_ca_system_score_gemma":0.00005131576,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1519871,"about_ca_topic_score_gemma":0.3158803,"domain_scores_codex":[0.9989951,0.0001281615,0.0001347379,0.0001701343,0.0004348573,0.0001369983],"domain_scores_gemma":[0.9993257,0.00004804959,0.0002479851,0.0003194553,0.00003478697,0.00002400517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000852577,0.0001406797,0.9942727,0.000006043088,0.000007843265,0.000003681304,0.002691735,0.0007261314,0.000467652,0.000002101825,0.0001214247,0.001474744],"study_design_scores_gemma":[0.001057205,0.00005941397,0.9898088,0.00002099673,0.00006608721,1.474499e-7,0.0002528169,0.0007949021,0.007561495,0.0002807794,0.00002739762,0.00006998905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929206,2.617146e-7,0.00002521909,0.00003797299,0.0001136411,0.005644528,0.000006194628,0.000004052387,0.001247464],"genre_scores_gemma":[0.9994391,8.842029e-8,0.0002160517,0.00001399281,0.000005992339,0.0002383071,0.000001407893,0.000001889352,0.00008317977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1638932,"threshold_uncertainty_score":0.9973153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1211263137063011,"score_gpt":0.3054075513406396,"score_spread":0.1842812376343385,"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."}}