{"id":"W4281998059","doi":"10.1002/lom3.10497","title":"Aquatic particulate absorption coefficient combining extraction and bleaching optimized for inland waters","year":2022,"lang":"en","type":"article","venue":"Limnology and Oceanography Methods","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Université du Québec à Montréal; Université de Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Groupe de recherche interuniversitaire en limnologie","keywords":"Phycocyanin; Extraction (chemistry); Particulates; Attenuation coefficient; Absorption (acoustics); Phytoplankton; Algae; Environmental chemistry; Biomass (ecology); Environmental science; Correlation coefficient; Pigment; Chemistry; Cyanobacteria; Botany; Materials science; Chromatography; Nutrient; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002885055,0.000489669,0.0003931616,0.000542798,0.000379136,0.0004029737,0.0003411252,0.0003642666,0.0007944703],"category_scores_gemma":[0.0003690377,0.000255566,0.0003764396,0.0005800289,0.0002082354,0.0002918146,0.0003403675,0.0003673401,0.0005480787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000464271,"about_ca_system_score_gemma":0.0005576794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007727318,"about_ca_topic_score_gemma":0.013678,"domain_scores_codex":[0.9997995,0.00001216839,0.00001179663,0.00006626332,0.00008377714,0.00002650822],"domain_scores_gemma":[0.9998159,0.00002659865,0.00003313187,0.0000177888,0.00009727991,0.000009318937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004745903,0.00003250694,0.001497586,0.0000470928,0.000009924415,0.0000226389,0.00002711334,0.0007685666,0.9873219,0.00004066729,0.0001123698,0.0100722],"study_design_scores_gemma":[0.000006966322,0.00007870105,0.009287181,0.000004589298,0.00003538657,0.00004166075,0.00002468365,0.01121364,0.9780897,0.000027306,0.001171303,0.00001888348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9022392,0.0004665391,0.09354359,0.0000598269,0.00003955825,0.00008807312,0.0005839582,0.001035799,0.001943436],"genre_scores_gemma":[0.8295251,0.0005586013,0.1637048,0.00008839683,0.00001563377,0.0001519437,0.001305808,0.000306967,0.004342762],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007727318,"threshold_uncertainty_score":0.01536471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02168191289280307,"score_gpt":0.2838330185780721,"score_spread":0.2621511056852691,"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."}}