Bibliographic record
Abstract
Results of EuGene annotation on the M. javanica genome. Predictions of gene models in M. javanica genome were done with the fully automated pipeline EuGene-EP version 1.6.5 (Sallet et al., 2019). EuGene has been configured to integrate similarities with known proteins of Caenorhabditis elegans (PRJNA13758) downloaded from Wormbase ParaSite (Howe et al., 2017) as well as the “nematoda” section of UniProtKB/Swiss-Prot library (UniProt Consortium, 2018), with the prior exclusion of proteins that were similar to those present in RepBase (Bao et al., 2015). We used as transcriptional evidence, transcriptome data for M. incognita, as it is the Meloidogyne species with the most comprehensive expression data available. RNA-seq data from pre-parasitic J2, J2-J3 and adult female stages (Blanc-Mathieu et al., 2017) were assembled de novo using Trinity (Haas et al., 2013) followed by a cleanup that retains for each trinity locus only the transcript that gives the longest ORF. The dataset of M. incognita assembled transcriptome was aligned on the genomes of the four Meloidogyne species using Gmap (Wu and Watanabe, 2005) and except for M. incognita the option "cross-species" was used. Only alignments spanning 30% of the transcript length with at least 97% identity were retained. The EuGene default configuration was edited to set the “preserve” parameter to 1 for all datasets, the “gmap_intron_filter” parameter to 1, the minimum intron length to 35 bp, and to allow the non-canonical donor splice site “GC”. Finally, the Nematode specific Weight Array Method matrices were used to score the splice sites (available at this URL: http://eugene.toulouse.inra.fr/Downloads/WAM_nematodes_20171017.tar.gz).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".