{"id":"W2608581474","doi":"10.1111/jvs.12536","title":"Divergence between riparian seed banks and standing vegetation increases along successional trajectories","year":2017,"lang":"en","type":"article","venue":"Journal of Vegetation Science","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada; McGill University; Université Laval","funders":"Environment and Climate Change Canada; National Wildlife Research Center; Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Riparian zone; Species richness; Soil seed bank; Ecology; Ecological succession; Riparian forest; Plant community; Vegetation (pathology); Bank; Secondary succession; Geography; Habitat; Biology; Agroforestry; Seedling; Agronomy","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001685572,0.0001129604,0.000199041,0.0001317357,0.001995408,0.000224677,0.0004981636,0.00004832836,0.0000314803],"category_scores_gemma":[0.001177802,0.00009279585,0.00004202055,0.0002011499,0.001497805,0.002739406,0.0002188376,0.0001637314,0.00001381526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001313893,"about_ca_system_score_gemma":0.00008882675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008876887,"about_ca_topic_score_gemma":0.0003710021,"domain_scores_codex":[0.9984155,0.00006025531,0.0004147271,0.0002224933,0.000672875,0.0002142056],"domain_scores_gemma":[0.9984334,0.0002461089,0.0008727697,0.0001527981,0.0001432847,0.0001516505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001249688,0.00002127124,0.9906121,0.000009557475,0.00001158334,0.00000639876,0.0008754709,0.0006961144,0.004999132,0.0007744642,0.000007427904,0.001973982],"study_design_scores_gemma":[0.0003519405,0.00009084227,0.9930968,0.00005590484,0.00003181125,0.0000203245,0.0001281093,0.001603129,0.0006720482,0.003814661,0.0000263618,0.0001080504],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932286,0.0001376689,0.004343723,0.0003420928,0.0003831529,0.00007222068,0.00000159014,0.000007408446,0.001483574],"genre_scores_gemma":[0.9968995,0.00006292153,0.00286333,0.0000484417,0.00007936484,0.000002079051,6.188104e-7,0.000004388006,0.00003937446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004327083,"threshold_uncertainty_score":0.9993039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02205948489133387,"score_gpt":0.2981323530099265,"score_spread":0.2760728681185926,"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."}}