{"id":"W2808323252","doi":"10.12957/jse.2018.34815","title":"GEOCHEMICAL NORMALIZERS APPLIED TO THE STUDY OF THE PROVENANCE OF LITHOGENIC MATERIALS DEPOSITED AT THE ENTRANCE OF A COASTAL LAGOON. A CASE STUDY IN AVEIRO LAGOON (PORTUGAL) / NORMALIZADORES GEOQUÍMICOS APLICADOS AO ESTUDO DE PROVENIÊNCIA DE MATERIAIS LITOGÉNICOS DEPOSITADOS NA ENTRADA DE UMA LAGUNA COSTEIRA. UM ESTUDO DE CASO NA LAGUNA DE AVEIRO (PORTUGAL)","year":2018,"lang":"en","type":"article","venue":"Journal of Sedimentary Environments","topic":"Geochemistry and Elemental Analysis","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Emera; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Provenance; Geology; Sediment; Lithology; Granulometry; Geochemistry; Diagenesis; Sedimentary rock; Erosion; Bioturbation; Mineralogy; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009686904,0.0004296745,0.0002514168,0.002428218,0.0004488285,0.0007743326,0.0002778277,0.0002753647,0.0006714509],"category_scores_gemma":[0.001303277,0.0002538607,0.0003356481,0.00244881,0.0006401916,0.000184043,0.0004835438,0.0002096259,0.0002658126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007336568,"about_ca_system_score_gemma":0.000663796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01517309,"about_ca_topic_score_gemma":0.03412352,"domain_scores_codex":[0.9993635,0.00009935891,0.00005543379,0.0001486771,0.0002909307,0.00004212895],"domain_scores_gemma":[0.999262,0.0001069562,0.0002573955,0.00008982724,0.0002606983,0.00002297905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0009516931,0.00009501564,0.36568,0.0005130431,0.0001859611,0.001405251,0.002112058,0.006125686,0.4640225,0.001299366,0.0006218577,0.1569876],"study_design_scores_gemma":[0.00001634906,0.0003552255,0.8020259,0.00006068395,0.000127939,0.001050347,0.001713048,0.008156547,0.1771526,0.0005058421,0.008788656,0.0000468885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774495,0.0005202392,0.01732448,0.00004453503,0.00002542137,0.00009385285,0.00119087,0.000184576,0.003166571],"genre_scores_gemma":[0.9732893,0.0003997192,0.02350696,0.00001655643,0.000007838873,0.00004925028,0.0007984189,0.00005822792,0.001873684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01517309,"threshold_uncertainty_score":0.03016961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006510363722887502,"score_gpt":0.2094971415549332,"score_spread":0.2029867778320457,"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."}}