{"id":"W4402617500","doi":"10.1093/plankt/fbae046","title":"Combining <i>in situ</i> and <i>ex situ</i> plankton image data to reconstruct zooplankton (&amp;gt;1 mm) volume and mass distribution in the global ocean","year":2024,"lang":"en","type":"article","venue":"Journal of Plankton Research","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Canada Foundation for Innovation; European Molecular Biology Laboratory","keywords":"Zooplankton; Plankton; In situ; Biomass (ecology); Oceanography; Sampling (signal processing); Temperate climate; Environmental science; Latitude; Biology; Atmospheric sciences; Ecology; Geology; Meteorology; Physics","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":[0.0003639513,0.0006333291,0.0003003534,0.001328289,0.0001783036,0.0007201727,0.0003211281,0.0002943509,0.001823594],"category_scores_gemma":[0.0004263285,0.0002528848,0.0004722061,0.001100571,0.0001556947,0.0006481051,0.0006870457,0.000250487,0.0004910548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001828299,"about_ca_system_score_gemma":0.0002333651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002611536,"about_ca_topic_score_gemma":0.007187252,"domain_scores_codex":[0.9998523,0.00001875605,0.00001152917,0.00004886823,0.00004670472,0.00002180603],"domain_scores_gemma":[0.9997911,0.00003667447,0.00004699042,0.00003610523,0.00007088699,0.00001831898],"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":[0.0002658512,0.0001294164,0.1993726,0.0004841889,0.0004311115,0.0003084937,0.0002745352,0.02064446,0.5966718,0.0005064817,0.001510487,0.1794006],"study_design_scores_gemma":[0.00002300032,0.0001578479,0.5867857,0.00007923679,0.0003133446,0.0006707306,0.0005205248,0.2016978,0.2032249,0.0007937487,0.005641596,0.0000915397],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9293252,0.0003556476,0.0653853,0.00006925561,0.0000431363,0.0000218218,0.001644063,0.0006147383,0.002540982],"genre_scores_gemma":[0.9024844,0.0002618607,0.09357946,0.00005737572,0.00002714638,0.00003488145,0.002024176,0.0001492857,0.0013815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002611536,"threshold_uncertainty_score":0.006100535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05437588989067556,"score_gpt":0.3146485651745166,"score_spread":0.2602726752838411,"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."}}