{"id":"W2951856979","doi":"","title":"GIS Technology Helps Rid Southeast Asia of Landmines and UXO","year":2008,"lang":"en","type":"article","venue":"JMU Scholoraly Commons (James Madison University)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mine action; Southeast asia; Remote sensing; Environmental science; Geography; Mining engineering; Geology; Political science; History; Ancient history; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009685096,0.0007671041,0.0003846065,0.002475595,0.001143428,0.002451691,0.0005493726,0.0005589616,0.03246412],"category_scores_gemma":[0.002277878,0.0003929682,0.0003815568,0.003253413,0.0007616788,0.003045644,0.002629814,0.0007716185,0.008250279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004521755,"about_ca_system_score_gemma":0.001097144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00561966,"about_ca_topic_score_gemma":0.01221051,"domain_scores_codex":[0.9997326,0.00008651022,0.00002404901,0.00004245513,0.00008695971,0.00002744881],"domain_scores_gemma":[0.9989213,0.0003424716,0.00008668321,0.0002806924,0.0002666918,0.0001021565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001119865,0.00007711975,0.02133828,0.0005543791,0.00008241328,0.0009664082,0.008441587,0.002030414,0.007125977,0.02751801,0.325136,0.6066174],"study_design_scores_gemma":[0.0000292329,0.0000422101,0.007286636,0.0002986001,0.00004891882,0.0007764577,0.006324216,0.004886495,0.004629585,0.02395369,0.9516822,0.00004187702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1026031,0.01016763,0.3082842,0.05554005,0.002657902,0.0003156624,0.01355553,0.03694207,0.4699337],"genre_scores_gemma":[0.4341088,0.01562477,0.3779276,0.004577714,0.0008663028,0.0003145957,0.008981328,0.004068475,0.1535304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03246412,"threshold_uncertainty_score":0.1086033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450715140680977,"score_gpt":0.1899367520866662,"score_spread":0.1754296006798564,"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."}}