{"id":"W4394245318","doi":"10.6084/m9.figshare.3516020","title":"Appendix A. Unpublished data sources for population status of green crabs in California, Oregon, Washington, British Columbia, and Alaska 1994–2006.","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Crustacean biology and ecology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Archaeology; Population; Fishery; Demography; Biology; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001296823,0.001032514,0.0009527227,0.004125539,0.000695464,0.001092798,0.001742499,0.0006343255,0.2226423],"category_scores_gemma":[0.008906248,0.0007978844,0.0006668761,0.01119368,0.0002818158,0.001124764,0.001215768,0.0009841247,0.07371014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002430952,"about_ca_system_score_gemma":0.00404736,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.116602,"about_ca_topic_score_gemma":0.1992955,"domain_scores_codex":[0.998956,0.0001072116,0.000258246,0.0002033622,0.000333179,0.0001419104],"domain_scores_gemma":[0.9905664,0.002037815,0.001287922,0.000647107,0.004997004,0.0004636865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000329724,0.0000206077,0.002735267,0.0007146665,0.00003136413,0.00001373311,0.00002312205,0.0002012235,0.00003477263,0.0002916846,0.9935046,0.002395933],"study_design_scores_gemma":[0.0003011314,0.00002264835,0.03453504,0.0007523972,0.00006477823,0.00004950423,0.0001609225,0.0001752116,0.0001577932,0.001058625,0.9626898,0.00003220313],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005628408,0.00001441577,0.00002427573,0.00001166484,0.000007944344,0.0000122368,0.9993368,0.00001680354,0.0005195139],"genre_scores_gemma":[0.0006692956,0.00007187269,0.0003129538,0.00003515324,0.000007123667,0.000251058,0.996797,0.00003164177,0.00182383],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8833979,"threshold_uncertainty_score":0.7448128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199840678046782,"score_gpt":0.245561633536819,"score_spread":0.2235632267563512,"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."}}