{"id":"W4307283108","doi":"10.1002/ecs2.4268","title":"Using successional drivers to understand spatiotemporal dynamics in intertidal mudflat communities","year":2022,"lang":"en","type":"article","venue":"Ecosphere","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Allison University; University of Victoria; University of New Brunswick","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Fonds en Fiducie pour la Faune du Nouveau-Brunswick; Environment and Climate Change Canada; Nature Conservancy of Canada; Mount Allison University; Mitacs; University of New Brunswick; Employment and Social Development Canada; University of Victoria","keywords":"Ecological succession; Intertidal zone; Ecology; Biological dispersal; Disturbance (geology); Habitat; Ecosystem; Predation; Competition (biology); Community; Biology; Population","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001169915,0.00007883298,0.0001058107,0.00004281773,0.0002060304,0.00002398846,0.000205163,0.00002633756,0.01996958],"category_scores_gemma":[0.000003408536,0.00007511366,0.00002387648,0.0001532227,0.00003008129,0.00009562199,0.0001084226,0.0001959067,0.00004592557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004702762,"about_ca_system_score_gemma":0.00005219998,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07452673,"about_ca_topic_score_gemma":0.5912872,"domain_scores_codex":[0.9993625,0.0001004463,0.0001306529,0.0001048401,0.0001171157,0.0001844041],"domain_scores_gemma":[0.999756,0.00005798299,0.00003412087,0.00008968637,0.000007275794,0.00005491434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001507018,0.00002162386,0.9211257,0.0000086592,0.000009857152,0.00005354155,0.0007512544,0.04799823,0.000003887212,0.0004405381,0.003327798,0.02610821],"study_design_scores_gemma":[0.0006250878,0.0005227414,0.1826385,0.00002765481,0.000006986009,0.00007718568,0.09680426,0.7007976,0.00001020585,0.001747863,0.01629245,0.0004494858],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.930364,0.00002846163,0.0001458839,0.0002600643,0.0001993119,0.00007495415,0.0001517772,0.00001345309,0.06876206],"genre_scores_gemma":[0.9975954,0.000003675023,0.0003466819,0.0003976994,0.00002036208,4.587023e-7,0.000442257,0.000002207039,0.00119128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7384872,"threshold_uncertainty_score":0.9809263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03191047064293297,"score_gpt":0.2311918931592387,"score_spread":0.1992814225163057,"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."}}