{"id":"W6887984685","doi":"10.18164/632b3d23-8440-4d77-9769-62770ad22dc1","title":"Inventaire canadien des terres humides (ICTH) ; Région de Montmagny","year":2008,"lang":"fr","type":"dataset","venue":"ECCC Data Catalogue","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Canada; Environment and Climate Change Canada","funders":"","keywords":"Bustard; Statistical analysis; South asia; Viet nam","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.001850937,0.002442557,0.002039347,0.001245873,0.001897083,0.0004663316,0.01230038,0.001691887,0.0008361293],"category_scores_gemma":[0.002889585,0.002815628,0.0003834964,0.001490765,0.004497968,0.004024656,0.007330214,0.002556061,0.05665182],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005327704,"about_ca_system_score_gemma":0.003502622,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7140436,"about_ca_topic_score_gemma":0.939307,"domain_scores_codex":[0.9874537,0.001218978,0.002076586,0.00390306,0.001426932,0.003920753],"domain_scores_gemma":[0.9823119,0.0004962205,0.001381959,0.0133401,0.0002954164,0.002174377],"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":[0.0001747622,0.0008326005,0.006436151,0.001193932,0.0007823054,0.008628543,0.001370035,0.00008606689,0.0001153563,0.00004356234,0.9786134,0.00172331],"study_design_scores_gemma":[0.001246277,0.0003638256,0.007312472,0.001611738,0.001166975,0.005951561,0.0007707754,0.0005047917,0.0002644587,0.0002956717,0.9778155,0.002695923],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02101304,0.01660706,0.00002257779,0.0003669884,0.001802928,0.001369469,0.9582834,0.0003536695,0.0001808987],"genre_scores_gemma":[0.003484988,0.006206694,0.001493675,0.000579829,0.002245459,0.0002079414,0.983818,0.0007086784,0.001254761],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2252634,"threshold_uncertainty_score":0.9997451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07894708405958101,"score_gpt":0.2956910876765474,"score_spread":0.2167440036169664,"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."}}