{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008870737,0.00007685316,0.0000966705,0.00005963619,0.0002825373,0.0001089461,0.0002186601,0.00001804918,0.0001093657],"category_scores_gemma":[0.000142767,0.00003882938,0.00002622559,0.0002929955,0.0004476501,0.0004057041,0.00002501996,0.00002981356,0.00001362143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007290707,"about_ca_system_score_gemma":0.00003102197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002958221,"about_ca_topic_score_gemma":0.0002266517,"domain_scores_codex":[0.9991201,0.00003369007,0.0001166883,0.0002487926,0.0002500139,0.0002306788],"domain_scores_gemma":[0.9982435,0.001339024,0.00006786705,0.0001689714,0.0001093717,0.00007127458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000179753,0.000007303115,0.8498788,0.00004729812,0.000005651245,6.832665e-7,0.002196296,0.00005747093,0.009144322,0.00003542651,0.000004380812,0.1386043],"study_design_scores_gemma":[0.0002173533,0.0001110014,0.9823977,0.0001779634,0.00001358257,0.00001121496,0.0003600462,0.002689757,0.01364411,0.0002736514,0.00003304645,0.00007056207],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948886,0.001039841,0.001471365,0.001365439,0.0007580789,0.0003564493,0.00005186373,0.000006814534,0.00006151668],"genre_scores_gemma":[0.9987871,0.0003326143,0.0006097036,0.0001445431,0.0000763846,0.000006789442,0.000003678189,0.000001769293,0.00003738358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1385338,"threshold_uncertainty_score":0.2173077,"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."}}