{"id":"W4281478481","doi":"10.1007/s12237-022-01090-w","title":"Within-marsh and Landscape Features Structure Ribbed Mussel Distribution in Georgia, USA, Marshes","year":2022,"lang":"en","type":"article","venue":"Estuaries and Coasts","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Georgia Sea Grant, University of Georgia; Department of Natural Resources, Government of Newfoundland and Labrador; Georgia Southern University; National Oceanic and Atmospheric Administration; U.S. Department of Commerce","keywords":"Mussel; Marsh; Salt marsh; Environmental science; Ecology; Landscape ecology; Fishery; Wetland; Biology; Habitat","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001529239,0.0001391963,0.0001575752,0.00002216654,0.0003298574,0.00007610307,0.000105436,0.0000466731,0.0006541359],"category_scores_gemma":[0.00002553715,0.000124595,0.00001741937,0.000165269,0.0001049131,0.0001305481,0.0006565196,0.0002049875,0.000004992665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008481512,"about_ca_system_score_gemma":0.00001219213,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00253817,"about_ca_topic_score_gemma":0.04312851,"domain_scores_codex":[0.9990563,0.00005613373,0.0001526273,0.0002923969,0.0002148119,0.0002277598],"domain_scores_gemma":[0.999674,0.0000581661,0.00006111764,0.0001312627,0.000003145956,0.00007228151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000910482,0.00003222895,0.9875772,0.00002247019,0.000008048409,0.00002910073,0.001587882,0.001734002,0.0002384273,0.000614106,0.001612596,0.006452906],"study_design_scores_gemma":[0.0004790966,0.0001069111,0.9847951,0.000008738302,0.00001047656,0.0001187004,0.001318599,0.00395293,0.00001932705,0.001251,0.007735017,0.0002040933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985376,0.0001634147,0.00002418048,0.0003133501,0.0001480614,0.0001547167,0.0002306868,0.0000159661,0.0004120384],"genre_scores_gemma":[0.9989912,0.0000181475,0.00006377632,0.0000896647,0.0000192096,0.00001630374,0.0001350113,0.000009046803,0.0006576353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04059034,"threshold_uncertainty_score":0.9743319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002678727673161219,"score_gpt":0.1740607835322762,"score_spread":0.171382055859115,"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."}}