{"id":"W2326116590","doi":"10.1139/as-2015-0020","title":"What is the most efficient and effective method for long-term monitoring of alpine tundra vegetation?","year":2016,"lang":"en","type":"article","venue":"Arctic Science","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Tundra; Species richness; Vegetation (pathology); Abundance (ecology); Relative species abundance; Ecology; Environmental science; Physical geography; Plant community; Arctic; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.01078161,0.0008486329,0.001872018,0.002635784,0.001087932,0.0020744,0.001581211,0.002518748,0.00124417],"category_scores_gemma":[0.01879609,0.0005372277,0.0007243876,0.001687986,0.001207877,0.004456244,0.0007616737,0.0007702521,0.001092879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009427355,"about_ca_system_score_gemma":0.001424426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006520522,"about_ca_topic_score_gemma":0.02097988,"domain_scores_codex":[0.9939813,0.003206978,0.0004659314,0.00108289,0.001051866,0.0002110104],"domain_scores_gemma":[0.9803407,0.007183685,0.004457855,0.001467422,0.005359578,0.001190774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004674514,0.0004856528,0.4994049,0.001713232,0.0003635325,0.0002187838,0.0006426758,0.00233225,0.03559555,0.0004236893,0.004167766,0.4541844],"study_design_scores_gemma":[0.0001447365,0.00213341,0.9127616,0.001700625,0.0007830666,0.00147457,0.004035578,0.03026544,0.02672566,0.002143371,0.01744235,0.0003896195],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7404922,0.04241146,0.1911965,0.0113469,0.0006464017,0.001066179,0.001889137,0.001177234,0.009773987],"genre_scores_gemma":[0.5111265,0.008806292,0.4755396,0.0008835378,0.0007248187,0.0006247885,0.0005735785,0.0001612915,0.001559453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01078161,"threshold_uncertainty_score":0.05701923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03179177264422099,"score_gpt":0.3125005502427114,"score_spread":0.2807087775984904,"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."}}