{"schema_version":"papers/paper-detail-v1","title":"GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing","surface":"papers","opportunity_kernel":{"paper_id":"50a97cbe-a25a-49ea-a3b0-7bea5abca6ef","title":"GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing","authors":["Yanjie Li","Liping Zhang","Min Wu","Weijun Li","Lina Yu","Jingyi Liu","Yusong Deng","Mingzhu Wan","Xin Ning"],"arxiv_id":"2605.10685v1","doi":null,"published_at":"2026-05-11T15:00:22.000Z","score_object":{"overall":{"value":8,"scale":"0-10","confidence":0.85,"confidence_reason":"Backfilled from persisted papers.viability_score.","model_version":"phase0-web-fallback-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-05-12T20:15:23.286Z","fresh_until":"2026-06-11T20:15:23.286Z","is_stale":false,"source_count":1,"missingness":[]},"technical":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No canonical technical score row was available yet; marked low confidence.","model_version":"phase0-web-fallback-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-05-12T20:15:23.286Z","fresh_until":"2026-05-26T20:15:23.286Z","is_stale":true,"source_count":0,"missingness":["paper_score_objects.technical"]},"commercial":{"value":4,"scale":"0-10","confidence":0.7,"confidence_reason":"Backfilled from persisted commercial_flags and repo availability.","model_version":"phase0-web-fallback-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-05-12T20:15:23.286Z","fresh_until":"2026-06-11T20:15:23.286Z","is_stale":false,"source_count":1,"missingness":[]},"market":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No canonical market score row was available yet; marked low confidence.","model_version":"phase0-web-fallback-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-05-12T20:15:23.286Z","fresh_until":"2026-05-26T20:15:23.286Z","is_stale":true,"source_count":0,"missingness":["paper_score_objects.market"]},"team":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No canonical team score row was available yet; marked low confidence.","model_version":"phase0-web-fallback-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-05-12T20:15:23.286Z","fresh_until":"2026-05-26T20:15:23.286Z","is_stale":true,"source_count":0,"missingness":["paper_score_objects.team"]},"methodology":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No canonical methodology score row was available yet; marked low confidence.","model_version":"phase0-web-fallback-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-05-12T20:15:23.286Z","fresh_until":"2026-06-11T20:15:23.286Z","is_stale":false,"source_count":0,"missingness":["paper_score_objects.methodology"]}},"evidence_receipt":{"freshness":"fresh","proof_status":"unverified","repo_status":"missing","references_count":0,"source_count":0,"coverage":0,"missingness":["paper_evidence_receipts.references_count","paper_evidence_receipts.coverage"],"unresolved_unknowns":["Canonical evidence receipt has not been materialized yet."],"last_verification_at":"2026-05-12T20:15:23.286Z"},"lineage_hash":"8817d95cf31f769b4fe244bee9067e2dc4d9f373d894750c28d25a5b4540c591"},"distribution":null,"replication_evidence":[],"author_dna":[]}