{"id":"W2021653141","doi":"10.13031/2013.5223","title":"A HYDRO-SPATIAL HIERARCHICAL METHOD FOR SITING WATER HARVESTING RESERVOIRS IN DRY AREAS","year":2000,"lang":"en","type":"article","venue":"Applied Engineering in Agriculture","topic":"Water resources management and optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Environmental science; Arid; Irrigation; Surface runoff; Hydrology (agriculture); Spatial distribution; Surface water; Analytic hierarchy process; Water resources; Ranking (information retrieval); Water resource management; Environmental engineering; Geography; Remote sensing; Engineering; Computer science; Geology; Ecology; Operations research","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002230968,0.0002346018,0.0002256375,0.0001530921,0.00003626723,0.0000695642,0.0001664142,0.0001477831,0.00003096902],"category_scores_gemma":[0.00001115129,0.0001802995,0.00004775961,0.000278913,0.00000580164,0.0001081397,0.00003124513,0.0003180054,0.00001275693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008310664,"about_ca_system_score_gemma":0.000001470471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005827804,"about_ca_topic_score_gemma":0.0000981758,"domain_scores_codex":[0.9988247,0.00001116145,0.0003041739,0.0002551695,0.0001256154,0.0004791793],"domain_scores_gemma":[0.9997472,0.00005441248,0.00001221036,0.000120355,0.000008476905,0.00005730534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001470979,0.00001320876,0.0001848053,0.0001339427,0.00001358813,0.000006425495,0.0007839269,0.9826186,0.009289475,0.000107929,0.0001085367,0.006724841],"study_design_scores_gemma":[0.0008702506,0.0000125429,0.00311965,0.00008732353,0.00001053047,0.000001808154,0.00003961215,0.9801111,0.006296177,0.00007793997,0.009018592,0.0003545114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8509408,0.00005611431,0.13676,0.0001231845,0.000108283,0.0009928858,0.000005184748,0.0007569684,0.01025659],"genre_scores_gemma":[0.9543918,0.00001171655,0.04442166,0.00002708538,0.000262597,0.0002905605,0.0001696384,0.00005986323,0.0003651169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.103451,"threshold_uncertainty_score":0.73524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004691867966062083,"score_gpt":0.1817277231667014,"score_spread":0.1770358552006393,"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."}}