{"id":"W4414939975","doi":"10.59350/vw8k0-p5960","title":"EcoData Retriever now supports R and environmental data, and has more datasets","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Labrador Retriever; Software; Animal welfare; Animal health; Work (physics)","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":[0.0004567229,0.0001293617,0.0001538845,0.00005836453,0.000112377,0.0003851679,0.001351096,0.00003724821,0.0001501052],"category_scores_gemma":[0.00003923618,0.0001069001,0.000009180325,0.00009545295,0.0001589735,0.002044384,0.003786597,0.00007387937,0.00008827572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008202579,"about_ca_system_score_gemma":0.00001385534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006232416,"about_ca_topic_score_gemma":0.0001160994,"domain_scores_codex":[0.9985693,0.00003910782,0.0001708671,0.0007717983,0.0002472229,0.0002017705],"domain_scores_gemma":[0.9969866,0.00006421065,0.00005793174,0.002723125,0.000003006684,0.0001650864],"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":[0.000004956827,0.00008134684,0.05530664,0.00001478531,0.00005974255,0.00004839261,0.0001267457,0.000001569487,0.0003652575,0.001168239,0.8881335,0.0546888],"study_design_scores_gemma":[0.0003883551,0.00004443859,0.03741131,0.00000575474,0.00004213077,0.0001007507,0.00003620423,0.2592486,0.0003650892,0.0002358685,0.7018092,0.0003122123],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1037329,0.0004530664,0.8632823,0.01019915,0.0003261885,0.000428292,0.01990879,0.0002702528,0.001399054],"genre_scores_gemma":[0.9185526,0.0001360955,0.05349799,0.002135013,0.00006556163,0.000002110432,0.02487246,0.0000139874,0.0007241252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8148198,"threshold_uncertainty_score":0.4719723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719663435996984,"score_gpt":0.2366446119296559,"score_spread":0.219447977569686,"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."}}