DEFMap: Dynamics Extraction From cryo-em Map
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
This version contains clinfo/DEFMap (version: v1.0.1) and leave-one-out cross-validation (LOOCV) dataset (corresponding input test data and trained models) used in this paper. If you want to use the LOOCV models, refer to the inference command in README and replace the input and model with those in the LOOCV dataset. e.g. <pre><code class="language-bash">python 3dcnn_main.py infer --test_dataset loocv_data/test_input_data/test_fold_1.jbl -o loocv_data/trained_model/model_kf1.h5 --prediction_output result/prediction_kf1.jbl</code></pre> The joblib output file contains a python dictionary object (key: voxel coordinate, value: logarithm of RMSF), and a voxel-level correlation coefficient is calculated and displayed.<br>
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.010 |
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