{"id":"W4393724636","doi":"10.5281/zenodo.7216125","title":"Irish Drought Impacts Database v.1.0 (IDID)","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Irish; Database; Forestry; Geography; Environmental science; Computer science; Linguistics; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006239518,0.0003572195,0.0004443676,0.00122843,0.004468862,0.003393067,0.004931559,0.0001830316,0.4521702],"category_scores_gemma":[0.007214237,0.000340084,0.0002045556,0.002432103,0.0002236562,0.0006174587,0.004308216,0.001043816,0.02282147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002002492,"about_ca_system_score_gemma":0.00002013536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001624217,"about_ca_topic_score_gemma":0.000003747254,"domain_scores_codex":[0.9924972,0.001485604,0.0009626189,0.001283093,0.003113723,0.000657743],"domain_scores_gemma":[0.9951695,0.000257897,0.0006429279,0.00236816,0.001157013,0.0004045097],"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":[0.00006243364,0.0001598983,2.907232e-7,0.00004325608,0.00004157155,0.00004806093,0.0001614221,0.0001218541,0.00005763421,0.00008894254,0.9776927,0.0215219],"study_design_scores_gemma":[0.0003892866,0.0002112179,0.00002902208,0.00003100687,0.00004782532,0.0001227917,0.0005628699,0.0003789684,0.000005977269,0.0004115409,0.9974439,0.0003655734],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002312638,0.0002482879,0.001051,0.0006421329,0.0005830023,0.000458596,0.9804242,0.0004991759,0.0158624],"genre_scores_gemma":[0.0009875164,0.0008849056,0.0000955961,0.0004224166,0.0002266412,1.266924e-7,0.9953439,0.001045615,0.0009932596],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4293488,"threshold_uncertainty_score":0.9999051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.150599226228384,"score_gpt":0.3576715326508355,"score_spread":0.2070723064224514,"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."}}