{"id":"W2097129864","doi":"10.1371/journal.pone.0065697","title":"Model-Based Assessment of Estuary Ecosystem Health Using the Latent Health Factor Index, with Application to the Richibucto Estuary","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Frontier Geosciences (Canada); Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Dalhousie University; Commonwealth Scientific and Industrial Research Organisation","keywords":"Ecosystem health; Context (archaeology); Ecological health; Environmental science; Ecology; Estuary; Environmental resource management; Ecosystem; Geography; Ecosystem services; 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":[0.000801089,0.0006908107,0.0003981066,0.0006076915,0.0004963599,0.001344789,0.0006671915,0.0004974275,0.0005853843],"category_scores_gemma":[0.001460245,0.0002207038,0.0006828373,0.0004297454,0.0005889969,0.0003829612,0.001001416,0.000430695,0.00004031703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002243182,"about_ca_system_score_gemma":0.001704657,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1862102,"about_ca_topic_score_gemma":0.1322048,"domain_scores_codex":[0.9998512,0.00005015733,0.00000948104,0.00003981315,0.0000242443,0.00002506576],"domain_scores_gemma":[0.9994629,0.0002847723,0.00009146918,0.00002987562,0.00009410893,0.00003685144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001820417,0.00002567239,0.01192618,0.00001303726,0.00003208161,0.00003177784,0.00002414236,0.9837931,0.0003092415,0.001328548,0.0001411802,0.002356892],"study_design_scores_gemma":[0.000002932351,0.000008211881,0.00182302,0.000001786493,0.000005655393,0.000002877431,0.00001199477,0.9976377,0.00005262126,0.0003964759,0.00005257,0.000004214797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8674199,0.0003115532,0.125061,0.0006754879,0.00002766065,0.00007335345,0.0009895575,0.0004858441,0.004955639],"genre_scores_gemma":[0.9886113,0.00008886818,0.01023629,0.00001962191,0.000009444775,0.00003350227,0.0002223406,0.00001926673,0.0007593984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8137898,"threshold_uncertainty_score":0.3702526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06968498142477265,"score_gpt":0.2870131308086419,"score_spread":0.2173281493838693,"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."}}