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TREC 2020 PM run r1st (PINGAN NLP)

Submitted run as evaluated by the track.

1 evaluation · 1 result

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Submitted run as evaluated by the track.

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Configuration: TREC 2020 PM run r1st (PINGAN NLP)Protocol: TREC 2020 PM Phase 1: topical relevance of retrieved MEDLINE abstracts
Dataset: TREC 2020 Precision Medicine: 40 cancer-gene-treatment topics over a 2019 MEDLINE baseline snapshot		0.418 r-precision
unitless · higher

Uncertainty: Not reported by the source

Coverage: Not reported scored / Not reported eligible

Independent external evaluation · Source checked
Methods, coverage and source

r1st (PINGAN NLP), TREC 2020 PM Phase 1

egfrnsclc-20261009-protocol-trec2020-pm-phase1-relevance

Aggregation: Not reported

Overview of the TREC 2020 Precision Medicine Track; NIST TREC Browser, Precision Medicine 2020 runs page · Overview Table 5 (PDF page 9), block 'R-prec', row 3: team 'PINGAN NLP', run 'r1st'

Source checking is not independent reproduction. Release 2026-10-09-ba02f2f4a36e.

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Release 2026-10-09-ba02f2f4a36e · Record review: source checked

2 source records and release historyDownload this release (gzip)
Technical metadata and extraction receipts

Stable ID: egfrnsclc-20261009-config-trec2020-pm-r1st

areas
dna-genomes
contexts
clinical_research
method types
supervised_machine_learning
reported name
r1st
foundation model eligible
false
source locator
Overview Tables 5-6; TREC browser runs page, run r1st
missing metadata
version: reason: inapplicable; note: A submitted run is a fixed artifact; its MD5 is recorded where listed
parameters
Run type: automatic. Run description as submitted: this result generated by our retrieval model, we first retrieve pubmed es index, then, we manually labeled about 2000 pubmed papers. with these labeled papers, we train a relevance biobert model and tier biobert model. finally we pass all candiates papers to these to models. in this run ,we weight heavily on relevance
limitations
Listed as an automatic run, but its description says the team manually labelled about 2,000 PubMed papers to train its relevance and tier models; whether these labels concern the 2020 topics is not stated.
model identity note
Run provenance: run_id r1st; run_file_md5 9ac2c8909eaddba988b1cc0c70dfc66d; listed_in TREC browser runs page
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