Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present an effective method of eliminating unsolicited electronic mail (so-called spam ) and discuss its publicly accessible prototype implementation. A subscriber to our system is able to obtain an unlimited number of aliases of his/her permanent (protected) E-Mail address to be handed out to parties willing to communicate with the subscriber. It is also possible to set up publishable aliases, which can be used by human correspondents to contact the subscriber, while being useless to harvesting robots and spammers. The validity of an alias can be easily restricted to a specific duration in time, a specific number of received messages, a specific population of senders, and/or in other ways. The system is fully compatible with the existing E-Mail infrastructure and can be immediately accessed via any standard E-Mail client software (MUA). It can be easily deployed at any institution or organization running its private E-Mail server (MTA) with a trivial modification to that server. Our system offers a simple method to salvage the existing population of E-Mail addresses while eliminating all spam aimed at them.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it