{"id":"W4416598867","doi":"10.5256/f1000research.1014.s36713","title":"Green crab consumption model R code","year":2014,"lang":"en","type":"other","venue":"Faculty of 1000 Research Ltd","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Oceanic and Atmospheric Administration; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Washington State University","keywords":"","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011351,0.00168687,0.001210392,0.001479215,0.0004599314,0.001738705,0.001914083,0.001612239,0.283167],"category_scores_gemma":[0.009690659,0.0008947469,0.001934844,0.001487919,0.0004093538,0.001312158,0.0009535389,0.001423083,0.1587059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325072,"about_ca_system_score_gemma":0.002308086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02299063,"about_ca_topic_score_gemma":0.02675929,"domain_scores_codex":[0.9993784,0.0002255566,0.00002135956,0.0001867529,0.0001150732,0.00007281255],"domain_scores_gemma":[0.9980206,0.0009878575,0.0001010056,0.0004355141,0.000357823,0.000097161],"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":[0.000281807,0.0000724196,0.002767144,0.0005259028,0.0003864378,0.0001097168,0.00005053467,0.03964447,0.0003962834,0.0272289,0.8966284,0.03190792],"study_design_scores_gemma":[0.0007920627,0.0001070448,0.004154979,0.0004922524,0.0005280146,0.000279024,0.0001007081,0.1931502,0.001771771,0.1300718,0.668385,0.0001672979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01451881,0.001482553,0.1902277,0.004853393,0.001023152,0.0006681499,0.5889112,0.05131812,0.1469968],"genre_scores_gemma":[0.1197044,0.001762639,0.1401772,0.004016301,0.0003650814,0.002958018,0.2636726,0.05980257,0.4075412],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.283167,"threshold_uncertainty_score":0.9472881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1233584502488526,"score_gpt":0.4162296112651754,"score_spread":0.2928711610163228,"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."}}