{"id":"W4391998772","doi":"10.1038/s41597-024-02980-3","title":"Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Université de Montréal; University of British Columbia","funders":"Senter for Internasjonalisering av Utdanning; Universitetet i Bergen","keywords":"Ecosystem; Vegetation (pathology); Grassland; Biodiversity; Biomass (ecology); Ecology; Trait; Specific leaf area; Growing season; Environmental science; Plant functional type; Climate change; Geography; Threatened species; Biology; Agronomy; Botany","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001059861,0.00009890986,0.00009450274,0.00008118894,0.0001783029,0.00009985983,0.0005492967,0.00003602389,0.0001137925],"category_scores_gemma":[0.00006363841,0.00008027707,0.000006519658,0.0002757675,0.0004314544,0.001011156,0.0006080129,0.0001167784,0.0000779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525658,"about_ca_system_score_gemma":0.00004449776,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001961718,"about_ca_topic_score_gemma":0.02440344,"domain_scores_codex":[0.9985198,0.00004229864,0.0001745295,0.0008438738,0.0002313307,0.0001881235],"domain_scores_gemma":[0.9990539,0.00007251513,0.00004276932,0.0007816866,0.000005848959,0.00004331267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008133539,0.0003967388,0.4700567,0.0004400658,0.0001304363,0.0002452614,0.01529455,0.001020237,0.001834113,0.004274437,0.4461078,0.06011835],"study_design_scores_gemma":[0.0002557813,0.00003741642,0.4826041,0.00008275857,0.00003757409,0.00002438447,0.0002336304,0.4392119,0.000005181765,0.0006368269,0.07668851,0.0001820215],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913451,0.002338172,0.000613286,0.0008110824,0.001291272,0.000345403,0.001481771,0.0000720843,0.00170182],"genre_scores_gemma":[0.9907686,0.00005526983,0.0006044191,0.00005280753,0.00001591969,0.00001239125,0.007392111,0.000007722286,0.001090783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4381917,"threshold_uncertainty_score":0.9933987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03511588098095743,"score_gpt":0.2405348986465477,"score_spread":0.2054190176655902,"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."}}