{"id":"W6947879661","doi":"10.4224/3c8s-z290","title":"The gas meter image dataset (NRC-GAMMA)","year":2021,"lang":"en","type":"dataset","venue":"NRC Digital Repository","topic":"Subterranean biodiversity and taxonomy","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Automatic meter reading; Automation; Reading (process); Metre; Field (mathematics); Process (computing)","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":[0.0007193355,0.002043088,0.001308699,0.004359138,0.0009549806,0.001721605,0.002514378,0.002502987,0.008789389],"category_scores_gemma":[0.002324427,0.0004057476,0.001042439,0.005803236,0.0006166894,0.001646983,0.001950647,0.00164649,0.02068486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001858762,"about_ca_system_score_gemma":0.001583209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03461134,"about_ca_topic_score_gemma":0.08089595,"domain_scores_codex":[0.9985735,0.0001348197,0.0001125924,0.0004223529,0.0005462171,0.0002103741],"domain_scores_gemma":[0.9988851,0.0001327284,0.0001178771,0.000316986,0.0004115576,0.0001358288],"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":[0.0001655235,0.0001705949,0.003557974,0.001267535,0.00007396998,0.0002311023,0.0001256952,0.001404714,0.002847666,0.0006596095,0.9624476,0.02704795],"study_design_scores_gemma":[0.0001901229,0.0001022465,0.04701575,0.0004862651,0.00007407987,0.0009691122,0.0006652061,0.0122348,0.006223156,0.001816781,0.9300813,0.000141117],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007619645,0.001055908,0.001779696,0.0003676479,0.0001990777,0.0001444832,0.9751717,0.007972553,0.005689248],"genre_scores_gemma":[0.004979601,0.0001618513,0.003487861,0.00007805999,0.00001926226,0.00007855512,0.9902931,0.0001630957,0.0007386457],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03461134,"threshold_uncertainty_score":0.06881976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02148523068774518,"score_gpt":0.1919701520662398,"score_spread":0.1704849213784946,"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."}}