{"id":"W2775750220","doi":"10.62721/diffusion-fundamentals.22.828","title":"Determining the clog state of constructed wetlands using an embeddable Earth’s Field Nuclear Magnetic Resonance probe","year":2014,"lang":"en","type":"article","venue":"Diffusion fundamentals.","topic":"Advanced Scientific Techniques and Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Research Executive Agency; Universitat Politècnica de Catalunya; European Commission; Trent University; Nottingham Trent University","keywords":"Physics; Wetland; Earth (classical element); Field (mathematics); Nuclear magnetic resonance; Nuclear physics; Astronomy; Ecology; Mathematics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002176105,0.000111961,0.000123231,0.00002261296,0.0003331988,0.00004446862,0.0003008629,0.00003604529,0.001369573],"category_scores_gemma":[0.00002352615,0.00008265591,0.00003768261,0.0002016101,0.000295473,0.0001485344,0.000246523,0.0000997825,0.00003167521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002863959,"about_ca_system_score_gemma":0.000004722343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009629065,"about_ca_topic_score_gemma":0.00005831349,"domain_scores_codex":[0.9989252,0.00005285875,0.0002614091,0.0003029639,0.0002375804,0.0002199905],"domain_scores_gemma":[0.9993024,0.00005066135,0.0001389512,0.0004226109,0.00001199563,0.00007337918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003043665,0.0003317897,0.07417502,0.00001186448,0.000002233298,0.000001244285,0.0009669095,0.0002910635,0.5030434,0.0005723684,0.0007027363,0.4198709],"study_design_scores_gemma":[0.002024583,0.0014397,0.2999461,0.0001727347,0.00005085316,0.00004374344,0.001085286,0.1978353,0.1028805,0.01146981,0.3819762,0.001075044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926631,0.00001349246,0.004439516,0.00005986046,0.00006006918,0.0002977449,0.00001562674,0.00005678821,0.002393806],"genre_scores_gemma":[0.9825357,0.0000133995,0.01664998,0.0002939984,0.00001160168,0.00001282473,0.000007368188,0.00001428898,0.0004608919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4187959,"threshold_uncertainty_score":0.9995433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199631480646849,"score_gpt":0.2476159847937132,"score_spread":0.2356196699872447,"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."}}