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
<p>Visual depiction of one full theta-cycle training trial, separated into four different phases within the cycle (i.e., four <i>quarters</i>, each representing 50 ms). The CA1 learns to properly decode the CA3 pattern into the corresponding EC representation, while CA3 learns to encode the EC input in a more pattern-separated manner reflecting DG input. Arrows depict pathways of particular relevance for that quarter. <b>First Quarter</b>: Blue arrows show initial activation of CA3 and DG via monosynaptic pathways from ECin (superficial layers of EC). Green arrows show CA1 likewise being monosynaptically driven from ECin, and in turn driving ECout (deep layers) with bidirectional connectivity. <b>Second Quarter</b>: Red arrow indicates DG driving CA3, providing a target activity state over CA3 relative to the first quarter state. Also, CA3 starts to drive CA1, resulting in full “attempted recall” state over ECout by the end of the <b>Third Quarter</b>. <b>Fourth Quarter</b>: the ECin drives ECout (Orange arrow), which in turn drives any resulting changes in CA1. Note: The fourth quarter is the plus phase for all error-driven learning projections, the second quarter and the third quarter are the minus phase for CA3 → CA1, and the first quarter is the minus phase for ECin → CA1, CA3 → CA3, ECin → CA1, and CA1 ↔ ECout (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010589#sec003" target="_blank">Methods</a> for more details). Solid lines represent projections that have error-driven learning + Hebbian learning, dashed lines represent projections that only have Hebbian learning, dotted lines represent projections that do not learn in the model.</p>
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.918 | 0.001 |
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