{"id":"W2621598250","doi":"10.1016/j.dsr.2017.06.002","title":"Deep-water zooplankton in the Mediterranean Sea: Results from a continuous, synchronous sampling over different regions using sediment traps","year":2017,"lang":"en","type":"article","venue":"Deep Sea Research Part I Oceanographic Research Papers","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Gutenberg Forschungskolleg; European Commission; Consiglio Nazionale delle Ricerche; Generalitat de Catalunya; Ministerio de Economía y Competitividad; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Zooplankton; Oceanography; Mediterranean sea; Deep sea; Abundance (ecology); Trophic level; Sediment trap; Environmental science; Sediment; Water column; Geology; Mediterranean climate; Ecology; Biology; Paleontology","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","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.01989688,0.000498247,0.0006976654,0.001434921,0.006187973,0.001061094,0.003522641,0.0005963573,0.002954599],"category_scores_gemma":[0.002242191,0.0003017452,0.0002855491,0.0007907594,0.004437013,0.0005174939,0.0005653746,0.004191862,0.0002553253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009396875,"about_ca_system_score_gemma":0.0002947361,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01997661,"about_ca_topic_score_gemma":0.09830469,"domain_scores_codex":[0.9840149,0.006145698,0.0008889021,0.001583506,0.003394539,0.00397242],"domain_scores_gemma":[0.9901982,0.006039818,0.0001601965,0.002355529,0.0004422033,0.000804052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001843826,0.000282338,0.9794673,0.00006104861,0.0002942224,0.0008547609,0.002889802,0.0001962405,0.0003699579,0.00008788012,0.002682199,0.01097047],"study_design_scores_gemma":[0.002701244,0.0009317283,0.9434927,0.0001539631,0.00002713162,0.0000492488,0.003070384,0.02250443,0.0000983593,0.00157769,0.02488915,0.0005039308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827882,0.0006045259,0.00001054752,0.004749801,0.0004724511,0.001838968,0.0002102068,0.00005131472,0.009273959],"genre_scores_gemma":[0.9956799,0.001468597,0.00006321796,0.0001718542,0.0008044733,0.00007286517,0.001467426,0.00002551381,0.0002461327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07832807,"threshold_uncertainty_score":0.9999759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1417811255433167,"score_gpt":0.3654402119859844,"score_spread":0.2236590864426676,"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."}}