{"id":"W4404164166","doi":"10.1007/s12237-024-01439-3","title":"Eelgrass (Zostera marina) Trait Variation Across Varying Temperature-Light Regimes","year":2024,"lang":"en","type":"article","venue":"Estuaries and Coasts","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Zostera marina; Variation (astronomy); Trait; Seagrass; Environmental science; Oceanography; Fishery; Ecology; Biology; Geography; Geology; Ecosystem; Computer science; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001753813,0.0001678792,0.000172442,0.00004529263,0.0002813495,0.0005298723,0.0001107186,0.0001168675,0.0005315735],"category_scores_gemma":[0.00001827819,0.000123913,0.00004443765,0.0001710183,0.00006880643,0.0003037326,0.00004791342,0.000191802,0.00007022813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002182017,"about_ca_system_score_gemma":0.00003843484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005641559,"about_ca_topic_score_gemma":0.006386324,"domain_scores_codex":[0.9989968,0.00004374158,0.0001618202,0.0003431025,0.000129463,0.0003250953],"domain_scores_gemma":[0.9996044,0.0001372685,0.00002930528,0.0001160597,0.00002256262,0.0000903808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001622351,0.00001621464,0.04048235,0.0001663281,0.00009305216,0.0002386068,0.002685951,0.00004132516,0.0004046596,0.005395738,0.005886638,0.9444269],"study_design_scores_gemma":[0.0003993193,0.0004466514,0.3653753,0.0001069595,0.00004566765,0.0004817465,0.0005697972,0.004458016,0.000205765,0.005049524,0.6223055,0.0005557665],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9316261,0.003383372,0.0001028602,0.003876551,0.001479098,0.0002099475,0.0002707033,0.0002206153,0.05883069],"genre_scores_gemma":[0.9889669,0.0002098185,0.0001093992,0.0003338994,0.0002826725,0.00000231704,0.0002605733,0.000004655511,0.009829789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9438711,"threshold_uncertainty_score":0.8528387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007865424768860832,"score_gpt":0.2122043742159848,"score_spread":0.2043389494471239,"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."}}