{"id":"W2063878693","doi":"10.1016/j.jmarsys.2009.12.009","title":"How to build and use individual-based models (IBMs) as hypothesis testing tools","year":2009,"lang":"en","type":"article","venue":"Journal of Marine Systems","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; Dalhousie University","funders":"Fisheries and Oceans Canada; Mitacs; Natural Environment Research Council; Sight Research UK","keywords":"Calanus finmarchicus; Range (aeronautics); Ecology; Population; Computer science; Forcing (mathematics); Biology; Copepod; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008244502,0.0001405043,0.0002803445,0.0001164939,0.0000882079,0.0006696224,0.0003129879,0.00005845452,0.0001732268],"category_scores_gemma":[0.001118411,0.0001100659,0.00005583689,0.0003062595,0.0000416803,0.0008641502,0.0002316084,0.0002204629,0.00001035237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009436961,"about_ca_system_score_gemma":0.00002502848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008703209,"about_ca_topic_score_gemma":0.00002893064,"domain_scores_codex":[0.9983232,0.00009907503,0.000356697,0.0001765337,0.0007563826,0.0002880869],"domain_scores_gemma":[0.9988095,0.0004099814,0.0002178141,0.000205431,0.00005932672,0.0002979955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001677501,0.0001504729,0.3903337,0.00004588732,0.0000443112,0.0002531293,0.0001689468,0.009365386,0.002810937,0.0000784615,0.009623052,0.586958],"study_design_scores_gemma":[0.002844488,0.005056233,0.6838739,0.0002492054,0.0001198224,0.001593323,0.0006117555,0.03462444,0.0008137331,0.002791041,0.2663049,0.001117222],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9294928,0.00002295361,0.001241903,0.004313506,0.0001125493,0.0004160015,0.000006908254,0.00002159545,0.06437176],"genre_scores_gemma":[0.9875228,0.00001587064,0.008591877,0.000621673,0.0001664403,0.000004473558,6.529735e-7,0.00001452484,0.003061661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5858408,"threshold_uncertainty_score":0.6457185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0777708156826061,"score_gpt":0.2517624784649407,"score_spread":0.1739916627823345,"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."}}