{"id":"W6893376714","doi":"10.5281/zenodo.16608380","title":"METEOR 2.5D: An Open Geospatial Dataset of Spatiotemporal Evolution of Physical Vulnerability in UN-recognized Least Developed Countries (as of 2020) at Five-year Intervals, 1975-2030 (Part 1 of 2)","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Meteor (satellite); Geospatial analysis; Vulnerability (computing); Vulnerability assessment","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":[0.0005848227,0.001227988,0.0009084598,0.002742849,0.0003397365,0.001241885,0.001509886,0.001211692,0.01073574],"category_scores_gemma":[0.002937357,0.0003758237,0.001044936,0.005738324,0.0002657284,0.0007481,0.001257209,0.0009825152,0.009337267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116171,"about_ca_system_score_gemma":0.001835266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07182791,"about_ca_topic_score_gemma":0.09282166,"domain_scores_codex":[0.9994948,0.00007443326,0.00009010368,0.0001333306,0.0001360878,0.00007132331],"domain_scores_gemma":[0.9989945,0.0001910375,0.0001994042,0.0001780769,0.0003190142,0.0001179117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001601064,0.00005365599,0.01155856,0.00126846,0.0001788538,0.0001275838,0.00009084216,0.003720547,0.0004133054,0.001651603,0.9741464,0.006630059],"study_design_scores_gemma":[0.0002915538,0.00003711038,0.07261091,0.0004184252,0.00009939553,0.0001858231,0.0002958631,0.004047977,0.0008353743,0.002626815,0.9184595,0.00009136736],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005424848,0.00005186689,0.0001307898,0.00003522832,0.00001252455,0.000007058631,0.9986982,0.0001538047,0.0003679086],"genre_scores_gemma":[0.001640614,0.00004256146,0.0003502932,0.00001806858,0.000004873638,0.00003485688,0.997681,0.0000233109,0.0002044305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07182791,"threshold_uncertainty_score":0.1428196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03702924336084867,"score_gpt":0.2996973735320684,"score_spread":0.2626681301712198,"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."}}