{"id":"W2103197613","doi":"10.1046/j.1365-2419.2003.00227.x","title":"Intercalibrating SCOR, NORPAC and bongo nets and the consequences for interpreting decadal‐scale variation in zooplankton biomass in the Gulf of Alaska","year":2003,"lang":"en","type":"article","venue":"Fisheries Oceanography","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Zooplankton; Environmental science; Biomass (ecology); Oceanography; Scale (ratio); Hydrology (agriculture); Geology; Geography; Cartography; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009928982,0.0001147414,0.0001734353,0.00007546911,0.0001148354,0.000111759,0.000199271,0.00005732525,0.0001300632],"category_scores_gemma":[0.0002526273,0.00007173842,0.00004309903,0.0004909273,0.001145631,0.0002921919,0.0001225702,0.0001336935,2.297041e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001020955,"about_ca_system_score_gemma":0.00001022592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000686424,"about_ca_topic_score_gemma":0.00229397,"domain_scores_codex":[0.9988191,0.0002577997,0.000295892,0.0002245603,0.0001744191,0.0002283051],"domain_scores_gemma":[0.9991654,0.0005451467,0.00009339286,0.0001580349,0.00000926185,0.00002880388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009939852,0.00001966417,0.9911404,0.00003349457,0.000006393016,0.000001108499,0.006362804,0.000002038539,0.0002687029,0.0004322233,0.0003441018,0.001289642],"study_design_scores_gemma":[0.002372184,0.0003820669,0.9571877,0.00009190081,0.00002121478,0.00003284785,0.007463976,0.00258907,0.001503073,0.01024002,0.01776576,0.0003502294],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816973,0.00005038295,0.0003961134,0.001238095,0.00003489006,0.0004802468,0.000008183371,0.000006858093,0.01608789],"genre_scores_gemma":[0.9983388,0.00007337448,0.001241795,0.0002513766,0.000005919509,0.00004698997,0.000003898566,0.000007176724,0.00003070289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03395277,"threshold_uncertainty_score":0.4221126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008980902720354,"score_gpt":0.2254051691355707,"score_spread":0.2153153601083671,"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."}}