CP9B46 T5 native implementation commitment: source admission, native normalization and fixed scoring

This record binds the completed T5 native FAOSTAT adapter, annotation mappings, numerical scoring configuration and engineering evidence through a hash-only list and an implementation disclosure. Its historical normalization and synthetic numerical checks are engineering evidence, not blind predictive validation. Actual future agricultural-target admission and decoding require a separate explicit authorization and exact reveal-input receipt under the committed rules. Original V2/V3 predictions, exposure disclosures, scoring endpoints and NOT_MET judgments remain unchanged. Hash commitments do not prove absence of prior exposure or bias. No real target values, predictions, identity lists, private paths, model artifacts or credentials are released in these two documents.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-06
DOI
https://doi.org/10.5281/zenodo.23993910
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

CP9B46 T5 native implementation commitment: source admission, native normalization and fixed scoring

Weiwei Zhang
4 links
Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
article

CP9B46 T5 native implementation commitment: source admission, native normalization and fixed scoring

Weiwei Zhang
article en

Abstract

This record binds the completed T5 native FAOSTAT adapter, annotation mappings, numerical scoring configuration and engineering evidence through a hash-only list and an implementation disclosure. Its historical normalization and synthetic numerical checks are engineering evidence, not blind predictive validation. Actual future agricultural-target admission and decoding require a separate explicit authorization and exact reveal-input receipt under the committed rules. Original V2/V3 predictions, exposure disclosures, scoring endpoints and NOT_MET judgments remain unchanged. Hash commitments do not prove absence of prior exposure or bias. No real target values, predictions, identity lists, private paths, model artifacts or credentials are released in these two documents.

Zenodo (CERN European Organization for Nuclear Research)
University of Missouri (US)
Openalex Percentile: Top 19%
Topic Modeling
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