{"id":"W6948898142","doi":"10.5061/dryad.rfj6q57fj","title":"Regional plot x species data for alpine vegetation","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biological dispersal; Vegetation (pathology); Climate change; Species distribution; Spatial ecology; Biogeography; Spatial distribution; Null model","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":[0.0006733555,0.0003545837,0.0003566419,0.001403059,0.0002059035,0.0004550931,0.0004216199,0.0003939996,0.009388993],"category_scores_gemma":[0.003046468,0.0001487277,0.0006167711,0.001650274,0.0001905174,0.0004245512,0.0003386069,0.000528218,0.001641119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003247886,"about_ca_system_score_gemma":0.0001517172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01243275,"about_ca_topic_score_gemma":0.0142344,"domain_scores_codex":[0.9995843,0.0001563057,0.00002782132,0.000142299,0.00005468594,0.00003474406],"domain_scores_gemma":[0.9976501,0.001349535,0.0003208069,0.0003505441,0.000220812,0.0001082547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002270729,0.0005576327,0.4763143,0.001054119,0.001540037,0.0005996285,0.0009486557,0.3570517,0.00914624,0.006458858,0.07505964,0.06899852],"study_design_scores_gemma":[0.0004805846,0.0007348955,0.6360614,0.00009554659,0.0002118339,0.0006037285,0.0006513254,0.2500894,0.003319066,0.004317009,0.1033179,0.0001173414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7360589,0.0005410187,0.00655053,0.0001322912,0.00005468005,0.00006526793,0.245458,0.002883809,0.008255485],"genre_scores_gemma":[0.864273,0.0001342145,0.008951612,0.00003243416,0.00001733333,0.0001369384,0.1247147,0.0002393522,0.001500485],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01243275,"threshold_uncertainty_score":0.03140926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09530387322539015,"score_gpt":0.3760527666445489,"score_spread":0.2807488934191588,"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."}}