{"id":"W4231446028","doi":"10.4257/oeco.2013.1704.07","title":"A MONITORING TECHNIQUE FOR HIGH-ALTITUDE HEADWATER STREAMS: A CASE STUDY IN THE HIGH ANDES","year":2013,"lang":"en","type":"article","venue":"Oecologia Australis","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"Escuela Superior Politécnica de Chimborazo","keywords":"STREAMS; Effects of high altitude on humans; Altitude (triangle); Geology; Physical geography; Environmental science; Ecology; Hydrology (agriculture); Earth science; Remote sensing; Geography; Biology; Meteorology; Computer science; Geotechnical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0007203767,0.0002324531,0.0002316196,0.00007393654,0.0001922669,0.0001353824,0.000670569,0.0001917743,0.0003656631],"category_scores_gemma":[0.000104314,0.0001523388,0.00004498992,0.0002422444,0.0001996309,0.0003250224,0.0003084446,0.0002837591,0.0001213244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002952504,"about_ca_system_score_gemma":0.000006511056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0425818,"about_ca_topic_score_gemma":0.001558422,"domain_scores_codex":[0.9982846,0.0001691897,0.0003333165,0.0004445149,0.0002319395,0.0005363937],"domain_scores_gemma":[0.999037,0.0002470571,0.00008451124,0.0005742541,0.00001373595,0.00004342164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001046524,0.0006038721,0.9792218,0.00001684254,0.00002147964,0.0005723293,0.001658016,0.0001501433,0.01353831,0.00003359589,0.001427439,0.002745656],"study_design_scores_gemma":[0.0007247758,0.0009974664,0.8881122,0.00001967109,0.00002976057,0.0001741021,0.01277076,0.000005546846,0.09376883,0.002962768,0.00007435242,0.0003597121],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955153,0.000002751691,0.00008205584,0.0009759716,0.0001797545,0.002995606,0.00000891814,0.0002051439,0.00003446709],"genre_scores_gemma":[0.9869408,0.000002546998,0.008446655,0.0000262454,0.00006682744,0.004357981,0.000002614316,0.00001737862,0.000138946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09110959,"threshold_uncertainty_score":0.9637938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05197047896090667,"score_gpt":0.3136468589063974,"score_spread":0.2616763799454907,"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."}}