{"id":"W6943975729","doi":"10.17632/3fpdwcgtcj","title":"Harvey Lake Itrax XRF-CS","year":2018,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sediment; Sediment core; Square (algebra); High resolution; Sample (material); Hydrology (agriculture)","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","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.002565733,0.00111164,0.001074692,0.0004838729,0.0004800628,0.0003582669,0.01177954,0.0008644943,0.01738779],"category_scores_gemma":[0.001176263,0.001086858,0.0001206463,0.0006923377,0.0004019226,0.001092757,0.01101727,0.001338877,0.1517325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001891527,"about_ca_system_score_gemma":0.0006328255,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118726,"about_ca_topic_score_gemma":0.0549318,"domain_scores_codex":[0.9930837,0.0004584721,0.0009427064,0.002608929,0.001576377,0.001329852],"domain_scores_gemma":[0.9802468,0.0001653861,0.0007025856,0.01819742,0.0002243531,0.0004634904],"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":[0.00007215152,0.0002513551,0.000007239301,0.0001726629,0.0003517836,0.0001557877,0.000009958459,2.786921e-7,0.00002995273,0.000003735168,0.9986966,0.0002484435],"study_design_scores_gemma":[0.0006924316,0.00009287138,0.00003261093,0.0001913047,0.0006203365,0.00006658481,0.000007466257,0.00003460728,0.00003236634,0.00005645691,0.9969899,0.001183035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000007954868,0.0003715436,0.00001436589,0.00007322564,0.001824673,0.0006598892,0.9961872,0.0003633001,0.0004978878],"genre_scores_gemma":[9.408057e-7,0.0004422569,0.001139369,0.0005048341,0.002507829,0.00005259249,0.9936517,0.0003208058,0.001379643],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1343447,"threshold_uncertainty_score":0.9991581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1385991859107328,"score_gpt":0.3569571037847225,"score_spread":0.2183579178739896,"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."}}