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
Rust fungi include many species that are devastating crop pathogens. To develop resistant plants, a better understanding of rust virulence factors, or effector proteins, is needed. Thus far, only six rust effector proteins have been described: AvrP123, AvrP4, AvrL567, AvrM, RTP1, and PGTAUSPE-10-1. Although some are well established model proteins used to investigate mechanisms of immune receptor activation (avirulence activities) or entry into plant cells, how they work inside host tissues to promote fungal growth remains unknown. The genome sequences of four rust fungi (two Melampsoraceae and two Pucciniaceae) have been analyzed so far. Genome-wide analyses of these species, as well as transcriptomics performed on a broader range of rust fungi, revealed hundreds of small secreted proteins considered as rust candidate secreted effector proteins (CSEPs). The rust community now needs high-throughput approaches (effectoromics) to accelerate effector discovery/characterization and to better understand how they function in planta. However, this task is challenging due to the non-amenability of rust pathosystems (obligate biotrophs infecting crop plants) to traditional molecular genetic approaches mainly due to difficulties in culturing these species in vitro. The use of heterologous approaches should be promoted in the future.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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