{"id":"W6945950255","doi":"10.26023/czbb-0wem-vw0s","title":"CEOP/EOP-1: MAGS: BERMS Hourly Soil Heat Flux, Moisture, and Temperature Data Set. Version 1.0","year":2011,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Berm; Boreal; Heat flux; Water cycle; Hydrology (agriculture); Period (music); Soil water; Energy budget","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008478562,0.001925227,0.001325413,0.00211401,0.0005419336,0.00154361,0.003188128,0.002040028,0.02420508],"category_scores_gemma":[0.003277648,0.0008189104,0.001001752,0.00499122,0.0003247044,0.001293187,0.001035902,0.00167291,0.03333495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478062,"about_ca_system_score_gemma":0.001819531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05195376,"about_ca_topic_score_gemma":0.06453977,"domain_scores_codex":[0.9993599,0.00008735477,0.00008154716,0.000176706,0.0001820925,0.000112436],"domain_scores_gemma":[0.9986095,0.0002485054,0.0002206535,0.000298244,0.0004745636,0.0001485196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006519012,0.00003582744,0.002115399,0.0003001956,0.00003829805,0.00002748857,0.00002615464,0.0008973902,0.0001094168,0.0003057065,0.9942726,0.001806322],"study_design_scores_gemma":[0.0006193384,0.00004343125,0.03411837,0.0002643428,0.00006837974,0.0001310069,0.0001999873,0.004163269,0.001260876,0.001705064,0.9573288,0.00009707106],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000329804,0.00001948344,0.00005384144,0.00003041879,0.00001129842,0.000005930395,0.9991399,0.0002021548,0.0002071121],"genre_scores_gemma":[0.0006466337,0.00001419579,0.0001657272,0.00001331326,0.000003393047,0.0000314145,0.9988614,0.00003453625,0.0002293424],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05195376,"threshold_uncertainty_score":0.1033027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06369254815396719,"score_gpt":0.3169014044182294,"score_spread":0.2532088562642622,"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."}}