{"id":"W7155461276","doi":"","title":"Coupling terrestrial-aquatic linkages with the biodiversity effects of forest stands in Québec, Canada","year":2020,"lang":"en","type":"other","venue":"FreiDok plus (Universitätsbibliothek Freiburg)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Biodiversity; Coupling (piping); Ecosystem; Vegetation (pathology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004492403,0.0003287713,0.0002995427,0.001131737,0.001469082,0.001377817,0.001121703,0.0004514647,0.02005743],"category_scores_gemma":[0.001519929,0.0002037576,0.0006715053,0.002601919,0.0005142576,0.0004772637,0.0008566247,0.0004796658,0.0008551307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04381325,"about_ca_system_score_gemma":0.05146253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9993919,"about_ca_topic_score_gemma":0.9997335,"domain_scores_codex":[0.9996473,0.00004877274,0.00001518929,0.00004986235,0.00006998249,0.0001690335],"domain_scores_gemma":[0.9984481,0.000134568,0.0001351324,0.00004241117,0.0008111238,0.0004286144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004123055,0.0001823202,0.7951381,0.0004370634,0.0006077474,0.0004804588,0.001257281,0.01352605,0.001435715,0.01021838,0.09718984,0.07911469],"study_design_scores_gemma":[0.00004184827,0.00002488693,0.9706576,0.0001559768,0.00009360698,0.00003633868,0.001720533,0.005114945,0.0001681617,0.0006030476,0.02134381,0.00003923442],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8104519,0.005366945,0.001697229,0.008520006,0.000178406,0.00020423,0.108895,0.0002582024,0.06442796],"genre_scores_gemma":[0.9505458,0.001569097,0.001234938,0.0005610437,0.00002042033,0.00006105532,0.008299912,0.00005743706,0.03765037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04381325,"threshold_uncertainty_score":0.3178889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00880121057113643,"score_gpt":0.1861183296186168,"score_spread":0.1773171190474803,"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."}}