{"id":"W6977255580","doi":"10.6084/m9.figshare.20286780.v1","title":"Data set for \"Watershed position drives variability among organic carbon masses in restored wetland soils in the Long Point region of southern Ontario, Canada\"","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Educational Innovations and Challenges","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wetland; Soil water; Position (finance); Carbon fibers; Total organic carbon; Data set","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":[0.0006321761,0.001672482,0.0008419473,0.002302204,0.002082477,0.001606129,0.002427649,0.001477215,0.01737322],"category_scores_gemma":[0.003960449,0.0005515611,0.001427019,0.004688651,0.0008053224,0.0005796865,0.001431127,0.001554055,0.01395509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009952564,"about_ca_system_score_gemma":0.02064008,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9406824,"about_ca_topic_score_gemma":0.976132,"domain_scores_codex":[0.9993666,0.00004179154,0.00004196571,0.0001432743,0.0002309103,0.000175543],"domain_scores_gemma":[0.9969703,0.0003527178,0.0001487593,0.0003788314,0.001744887,0.0004044356],"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":[0.0001002746,0.00004396638,0.006613992,0.0004039252,0.00006607713,0.0000558449,0.0001289554,0.0006538685,0.0002951706,0.0005297879,0.9873607,0.003747366],"study_design_scores_gemma":[0.0004895542,0.00002729124,0.1135721,0.0004182671,0.00008382533,0.0000978986,0.0008059635,0.001885716,0.0008659753,0.0009025679,0.8807621,0.00008881186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001443784,0.000105419,0.00007604576,0.0001679035,0.00003649888,0.00002566458,0.9967148,0.0002705839,0.001159257],"genre_scores_gemma":[0.002656752,0.00007078146,0.0003073273,0.0000433168,0.000006354701,0.00006509565,0.9952198,0.00003479292,0.001595763],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05931765,"threshold_uncertainty_score":0.1193339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0829175733006348,"score_gpt":0.2818441139360182,"score_spread":0.1989265406353833,"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."}}