Bibliographic record
Abstract
This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>. <strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted. <strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".