{"id":"W1993627778","doi":"10.1371/journal.pone.0074694","title":"Resource Quantity and Quality Determine the Inter-Specific Associations between Ecosystem Engineers and Resource Users in a Cavity-Nest Web","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nest (protein structural motif); Ecology; Resource (disambiguation); Ecosystem; Ecosystem engineer; Biology; Environmental resource management; Environmental science; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007939765,0.0002251622,0.0003542404,0.001298461,0.0004782519,0.001450341,0.000308339,0.0004389095,0.002607166],"category_scores_gemma":[0.003608957,0.000361391,0.0002779324,0.0005056903,0.000673086,0.001181159,0.00120538,0.0003614491,0.0004391889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00025856,"about_ca_system_score_gemma":0.0001864527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001623212,"about_ca_topic_score_gemma":0.005925413,"domain_scores_codex":[0.999488,0.000107759,0.00005994099,0.000122349,0.0001217554,0.000100139],"domain_scores_gemma":[0.9962139,0.001030351,0.001296636,0.0003058682,0.0004933812,0.0006599061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000786147,0.00005538028,0.9822389,0.00003312289,0.00009697117,0.0000810376,0.0004193541,0.0001466863,0.0122847,0.0001288855,0.00003998242,0.004396444],"study_design_scores_gemma":[0.000001606977,0.00003101476,0.9987077,0.00000445147,0.00001559217,0.00009009689,0.000252886,0.000448433,0.0002066865,0.0001184921,0.0001183762,0.000004645043],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992381,0.00007475109,0.0002435627,0.000007630793,7.393405e-7,0.00000442583,0.00003371818,0.000003304652,0.0003938056],"genre_scores_gemma":[0.999258,0.00004269864,0.0004340019,0.000006914356,0.000001964169,0.000004006767,0.00007259889,0.000003313696,0.0001765138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002607166,"threshold_uncertainty_score":0.008721828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08477816044506425,"score_gpt":0.2435969702164363,"score_spread":0.158818809771372,"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."}}