{"id":"W7092290050","doi":"10.58052/iej9b068q","title":"11_RGHR_V1 Individual Sample Plant Structure bog labrador tea seeds","year":2024,"lang":"","type":"other","venue":"System for Earth Sample Registration (SESAR)","topic":"Tea Polyphenols and Effects","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bog; Sample (material); Vegetation (pathology); Population structure; Peat","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001226956,0.001922026,0.002644708,0.0008624819,0.0005072537,0.0009236271,0.0007278005,0.002103518,0.004338212],"category_scores_gemma":[0.001005325,0.001684508,0.001021453,0.0008397077,0.0003316606,0.0002631369,0.0001646312,0.001321387,0.001019512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003885849,"about_ca_system_score_gemma":0.001459053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006357813,"about_ca_topic_score_gemma":0.00631524,"domain_scores_codex":[0.9912501,0.0003883515,0.002297862,0.002331696,0.00189442,0.001837543],"domain_scores_gemma":[0.9937395,0.001321236,0.001700656,0.002007542,0.0003173894,0.0009136781],"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":[0.002121423,0.0007168596,0.002837217,0.1148052,0.009573238,0.0005299837,0.00682447,0.0001475531,0.003314479,0.07455952,0.7406543,0.04391567],"study_design_scores_gemma":[0.004300497,0.002124104,0.001569296,0.009120231,0.002941824,0.0006514908,0.002607334,0.0008895325,0.001254453,0.0006789111,0.9720192,0.001843152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02496962,0.04818684,0.09824794,0.003907516,0.05728299,0.0550333,0.6286355,0.005500496,0.07823585],"genre_scores_gemma":[0.756959,0.000191315,0.0123981,0.0004219849,0.01777372,0.0008349791,0.03775788,0.002117278,0.1715457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7319894,"threshold_uncertainty_score":0.9997583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02436604586292936,"score_gpt":0.2684253818073626,"score_spread":0.2440593359444332,"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."}}