{"id":"W4393564569","doi":"10.5281/zenodo.8213144","title":"Monthly lake water quality data (May-September 2022) following the Greenwood Fire in northeastern Minnesota v1.0","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Science Foundation","keywords":"Environmental science; Hydrology (agriculture); Water quality; Forestry; Geography; Geology; Ecology; Biology; Geotechnical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002329877,0.0003269722,0.000327491,0.0001341027,0.001495512,0.0007543043,0.003677037,0.0001974878,0.02279429],"category_scores_gemma":[0.0004514451,0.0002537506,0.00009301678,0.000594143,0.0003196795,0.0006046596,0.009536241,0.0005411394,0.1471797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000213057,"about_ca_system_score_gemma":0.000003614088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00274291,"about_ca_topic_score_gemma":0.002358962,"domain_scores_codex":[0.9958859,0.000940745,0.0006082715,0.001013191,0.0009103382,0.0006415745],"domain_scores_gemma":[0.9973186,0.0000507854,0.000150168,0.002295518,0.00004438055,0.0001405179],"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":[0.00003856593,0.00006624655,0.0001749237,0.00005531637,0.00004563031,0.00005160782,0.0001516888,0.00006454018,0.0002522357,0.000002577959,0.9963524,0.002744258],"study_design_scores_gemma":[0.0003617336,0.0000457075,0.004489735,0.00004720862,0.00003916495,0.00003656535,0.0001374932,0.0001289954,0.00003014075,0.00002456707,0.994343,0.0003157151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09262943,0.00002044561,0.000005766924,0.0007600292,0.0008374142,0.0008102614,0.9021248,0.0003413562,0.002470549],"genre_scores_gemma":[0.007364721,0.00005130819,0.00001504442,0.00009713216,0.0002282705,2.947139e-7,0.990808,0.0009195852,0.0005157175],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1243854,"threshold_uncertainty_score":0.9999915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04899337174271547,"score_gpt":0.269679087288473,"score_spread":0.2206857155457575,"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."}}