{"id":"W6943521208","doi":"10.15468/lzrybq","title":"Elaboration du tarif de cubage multispécifique","year":2016,"lang":"fr","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Privy Council Office","funders":"","keywords":"Elaboration; Perspective (graphical); Mistake; Work (physics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001718903,0.003005499,0.001750604,0.007313831,0.001069825,0.002848059,0.002200242,0.001822839,0.02018844],"category_scores_gemma":[0.00625553,0.001053739,0.002352492,0.006669876,0.0006527257,0.001372399,0.001564276,0.002014093,0.02734591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002097387,"about_ca_system_score_gemma":0.003753468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1032358,"about_ca_topic_score_gemma":0.1841551,"domain_scores_codex":[0.998558,0.0002019205,0.0001208587,0.0005303222,0.0003465978,0.0002422825],"domain_scores_gemma":[0.9980107,0.0004984569,0.0001659175,0.0006140979,0.0005585082,0.0001523363],"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":[0.0003148554,0.0001180603,0.008032707,0.001008782,0.0002179987,0.00007170594,0.0001433499,0.003784814,0.001231855,0.001514648,0.9634563,0.02010493],"study_design_scores_gemma":[0.0005480627,0.00006463318,0.03386017,0.0004980723,0.0002077274,0.0002148106,0.0003662941,0.01407338,0.003264353,0.003516102,0.9432978,0.00008850059],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00323429,0.0002476104,0.001048645,0.0001043262,0.00005413966,0.00003362676,0.9918184,0.00244745,0.001011544],"genre_scores_gemma":[0.002645075,0.00008431738,0.00237942,0.00002203501,0.000009715589,0.00009308389,0.9937993,0.0001623924,0.0008046561],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1032358,"threshold_uncertainty_score":0.2052698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122000873073823,"score_gpt":0.2146191683214602,"score_spread":0.203399159590722,"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."}}