{"public_id":"cl_4ce2f0e5f24888a216ce6076fe4fa41a","status":"active","superseded_by_public_id":null,"corpus_id":232314408,"text":"The proposed machine learning methods mitigate accuracy variations in space and time, guaranteeing evenly high accuracy across China.","confidence":0.9,"paper":{"corpus_id":232314408,"title":"Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods","url":"https://sah.borca.ai/papers/232314408"},"contributors":[{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["extraction"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"origin_summary":{"object_type":"claim","status":"active","confidence":0.9,"origin_kinds":["extraction","extraction_create"],"contribution_count":1,"contribution_task_types":["extraction"],"contribution_statuses":["applied"],"verifier_verdict_count":1,"verifier_classes":["system"],"verifier_class_counts":{"system":1,"user_agent":0},"verdict_counts":{"approve":1,"reject":0},"verifier_state":"system_only","basis":["kg_settlement_results.decision_payload.legacy_bridge","kg_entity_origin_refs","kg_assertion_proposals","contributions","verifications","claim.status","claim.confidence"],"limits":["ledger provenance is aggregated; raw contribution and verifier audit rows are not expanded","entity matching uses settlement bridge refs and edge commands"]},"concepts":[{"public_id":"co_131bca549e7642f49f78d47f004aa2c5","name":"generalized regression neural network","description":"A machine learning method used to calibrate and optimize empirical Tm estimation in this study.","types":["method"],"url":"https://sah.borca.ai/concepts/co_131bca549e7642f49f78d47f004aa2c5"},{"public_id":"co_44334dd65b45bee6be0bef1f4e3017e3","name":"backpropagation neural network","description":"A machine learning method used to calibrate and optimize empirical Tm estimation in this study.","types":["method"],"url":"https://sah.borca.ai/concepts/co_44334dd65b45bee6be0bef1f4e3017e3"},{"public_id":"co_44dd26abbecc6b2eead3866114204f49","name":"weighted mean temperature","description":"A crucial parameter in estimating precipitable water vapor from tropospheric delay, used in GNSS-based water vapor monitoring.","types":["parameter"],"url":"https://sah.borca.ai/concepts/co_44dd26abbecc6b2eead3866114204f49"},{"public_id":"co_94d17d07c138a82f310ae8c0e0e6def1","name":"random forest","description":"A machine learning method used to calibrate and optimize empirical Tm estimation in this study.","types":["method"],"url":"https://sah.borca.ai/concepts/co_94d17d07c138a82f310ae8c0e0e6def1"}],"related_claims":[],"url":"https://sah.borca.ai/claims/cl_4ce2f0e5f24888a216ce6076fe4fa41a"}