{"id":"W6912153868","doi":"10.5281/zenodo.15778194","title":"Discovering Canadian Research and Government Data with Lunaris - 2025 DRI Connect poster","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Dementia Research Alliance","funders":"","keywords":"Metadata; Government (linguistics); Work (physics); Data sharing; Data collection; Research data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007776367,0.0006924932,0.0006022617,0.007916469,0.005994747,0.008822069,0.001628019,0.00125127,0.09723452],"category_scores_gemma":[0.009416307,0.0008295754,0.001178588,0.01506324,0.001170107,0.003843906,0.005148745,0.001998863,0.03419057],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01639283,"about_ca_system_score_gemma":0.03200769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7268395,"about_ca_topic_score_gemma":0.9198832,"domain_scores_codex":[0.9960674,0.0003961188,0.0001236172,0.0003976948,0.002479683,0.0005354302],"domain_scores_gemma":[0.9907769,0.001089073,0.000230495,0.001260649,0.00505798,0.001584926],"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":[0.00006338123,0.00001415551,0.0006990714,0.00006316503,0.00001167783,0.00004293327,0.0002643586,0.0002125802,0.0003651204,0.005603796,0.9703791,0.02228076],"study_design_scores_gemma":[0.00001959448,0.000007229928,0.001946281,0.00008020867,0.00001204682,0.00003292794,0.0005939428,0.0006753613,0.0004671943,0.002472655,0.9936543,0.00003834909],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01017756,0.003127411,0.02457007,0.04618827,0.005284086,0.001050346,0.4925611,0.01589305,0.4011481],"genre_scores_gemma":[0.04963681,0.00302797,0.1138562,0.005910865,0.001251057,0.000721731,0.5145111,0.00536651,0.3057177],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9911779,"threshold_uncertainty_score":0.5495383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.102570758138331,"score_gpt":0.3197605163381244,"score_spread":0.2171897581997933,"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."}}