Annual Report 2022: <i>The Plant Phenome Journal</i>
Bibliographic record
Abstract
The Plant Phenome Journal (TPPJ) from 2017-2021.I am now beginning my second year as Editor of TPPJ, finding it to be more exciting and gratifying than I could have ever imagined.In 2022, the TPPJ Editorial Board expanded to include two TEs from Canada and New Zealand and an AE from the Netherlands.This expansion has helped to raise visibility and grow the international reach of TPPJ.Since last year, the number of papers published in TPPJ has increased twofold, indicative of continued growth potential despite a competitive publishing environment.This past year also marked the successful completion of the first ever special section in TPPJ.The Belowground Phenotyping special section had a total of 8 articles that are sure to serve as a valuable resource to the plant science community.The establishment of a new partnership with the North American Plant Phenotyping Network (NAPPN) has laid a foundation for an NAPPN Annual Conference special section in 2023 and has strong potential to continue beyond 2023.In 2023, we expect to have a further expanded Editorial Board, multiple special sections, invited reviews, and an even larger volume of manuscript submissions.
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.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.123 | 0.095 |
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".