{"id":"W6976917814","doi":"10.6084/m9.figshare.19127771.v1","title":"Environmental and climatic metadata - feather moss bacteriome","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Moss; Transect; Metadata; Biomass (ecology); Taiga; Ecosystem; Boreal; Nutrient","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":[0.0005785677,0.001311714,0.001078611,0.003541379,0.0008499504,0.001771232,0.001989157,0.001550993,0.04147573],"category_scores_gemma":[0.003648045,0.0005016259,0.0009509383,0.009459008,0.0003866243,0.001005388,0.001306181,0.001207041,0.03374279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003397718,"about_ca_system_score_gemma":0.003964222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3775817,"about_ca_topic_score_gemma":0.5445185,"domain_scores_codex":[0.9994412,0.00006297087,0.00006651448,0.0001769546,0.0001315259,0.0001208649],"domain_scores_gemma":[0.9976053,0.0004392721,0.0002847463,0.0004298893,0.0009954274,0.0002454395],"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":[0.00007477232,0.0000207409,0.004872881,0.001006076,0.000060781,0.00004176469,0.0000556444,0.0006224597,0.0001997364,0.0007170637,0.9898444,0.002483642],"study_design_scores_gemma":[0.000145246,0.00001234545,0.02809873,0.0004499649,0.00004792118,0.00005594492,0.0001653922,0.0006524081,0.0003650694,0.0007760437,0.969182,0.00004897589],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000121422,0.00002212653,0.00001441411,0.00001612139,0.000003974149,0.00000252002,0.9996048,0.00004476276,0.000169905],"genre_scores_gemma":[0.000585929,0.00003213743,0.0001200562,0.00001951614,0.000002205991,0.00002325091,0.99887,0.00002095545,0.0003259954],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3775817,"threshold_uncertainty_score":0.7507679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06447488050413772,"score_gpt":0.2058677735483627,"score_spread":0.1413928930442249,"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."}}