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
The regulation mechanisms of any plant-pathogen interaction are complex and dynamic. A proteomic approach is necessary in understanding regulatory networks because it identifies new proteins in relation to their function and ultimately aims to clarify how their expression, accumulation and modification is controlled. One of the major control mechanisms for protein activity in plant-pathogen interactions is protein phosphorylation, and an understanding of the significance of protein phosphorylation in plant-pathogen interaction can be overwhelming. Due to the high number of protein kinases and phosphatases in any single plant genome and specific limitations of any technologies, it is extremely challenging for us to fully delineate the phosphorylation machinery. Current proteomic approaches and technology advances have demonstrated their great potential in identifying new components. Recent studies in well-developed plant-pathogen systems have revealed novel phosphorylation pathways, and some of them are off the core phosphorylation cascades. Additional phosphoproteomic studies are needed to increase our comprehension of the different mechanisms and their fine tuning involved in the host resistance response to pathogen attacks.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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