{"id":"W270785902","doi":"10.1016/j.ecss.2015.05.024","title":"Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data","year":2015,"lang":"en","type":"article","venue":"Estuarine Coastal and Shelf Science","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China; Natural Environment Research Council; Sight Research UK","keywords":"Phytoplankton; Comparability; Metadata; Sampling (signal processing); Computer science; Data quality; Environmental science; Range (aeronautics); Harmonization; Data collection; Ecology; Statistics; Biology; Mathematics; World Wide Web; Engineering; Telecommunications","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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.06447925,0.0009329902,0.001911699,0.01072587,0.001141052,0.006732744,0.002551372,0.002309335,0.004096389],"category_scores_gemma":[0.190481,0.0006297529,0.002099658,0.0129923,0.001854685,0.006786338,0.005909889,0.002702962,0.002053075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184126,"about_ca_system_score_gemma":0.002316407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004186598,"about_ca_topic_score_gemma":0.005095265,"domain_scores_codex":[0.9323837,0.02902094,0.01258478,0.009428094,0.01559561,0.0009869392],"domain_scores_gemma":[0.7415099,0.1078544,0.02628452,0.06778494,0.05443984,0.002126423],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001413147,0.0004652285,0.3672787,0.008554724,0.003102235,0.001182926,0.006661228,0.006489061,0.02404187,0.01475522,0.04513181,0.5209237],"study_design_scores_gemma":[0.0002349577,0.0003768259,0.6202174,0.004778702,0.001115649,0.001078294,0.005536162,0.009287251,0.01578792,0.04369187,0.2973658,0.0005291728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2485601,0.01740321,0.5398938,0.02276616,0.005583279,0.002826323,0.1287063,0.005363466,0.02889735],"genre_scores_gemma":[0.5076155,0.006269547,0.3814032,0.003833777,0.003321314,0.003222719,0.08941507,0.00201075,0.002908151],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9974486,"threshold_uncertainty_score":0.3410029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09351914849147427,"score_gpt":0.3247322935737211,"score_spread":0.2312131450822469,"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."}}