{"id":"W6908402372","doi":"10.26023/d5d6-t1q8-fe0k","title":"CEOP/EOP-1: MAGS: BERMS Old Black Spruce Site Raw Data. Version 1.0","year":2007,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Historical Influence and Diplomacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Berm; Black spruce; Hydrology (agriculture); Vegetation (pathology)","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":[0.0009601337,0.001938649,0.001064492,0.003449816,0.0006305008,0.001921777,0.002298153,0.001727524,0.03570721],"category_scores_gemma":[0.00385406,0.0007644679,0.001070727,0.005389946,0.0004126827,0.0010125,0.00158649,0.001840656,0.0633189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001309809,"about_ca_system_score_gemma":0.002477095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05993044,"about_ca_topic_score_gemma":0.104315,"domain_scores_codex":[0.9992774,0.00007912777,0.00007550584,0.0001850644,0.0001993059,0.0001836398],"domain_scores_gemma":[0.9981526,0.0002900671,0.0002393708,0.0004293923,0.0006075596,0.0002810422],"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":[0.00004569265,0.00002623478,0.001510508,0.0002015958,0.00002290107,0.00002035719,0.00003086437,0.0002292833,0.0001051025,0.0002056049,0.9961689,0.001432893],"study_design_scores_gemma":[0.0002793947,0.00002397326,0.02551186,0.0002177552,0.00003309803,0.00006535814,0.0002174688,0.00063515,0.0006386557,0.0008682602,0.9714592,0.0000499538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002342268,0.00002197565,0.00003343532,0.00002869477,0.00001487681,0.000005397525,0.999184,0.0002320891,0.0002453543],"genre_scores_gemma":[0.000414557,0.00001699499,0.0001405064,0.00001459056,0.000005110473,0.00002976463,0.9988324,0.00005765788,0.0004884777],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05993044,"threshold_uncertainty_score":0.1194525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017711851704555,"score_gpt":0.4226735853667404,"score_spread":0.320902400196285,"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."}}