Plant–environment interactions in Africa—Solutions to the challenges of environmental change
Bibliographic record
Abstract
Many aspects of environmental change represent a challenge to the plant sciences, from how to achieve sustainable crop production under climate change, to preserving plant diversity and the ecosystem services that plants provide in the face of land-use change. The need to find solutions to the problems posed by environmental change is particularly strong in African countries. For this reason, we are dedicating a P-EI Special Issue to this topic, which will be spread across two consecutive issues of the journal in 2022. All manuscripts submitted for consideration in this Special Issue will be subject to initial screening and if deemed suitable, will be subject to peer review. Article publication charges will be waived for authors of accepted articles from low-income, sub-Saharan African countries—see the following link for more information: https://authorservices.wiley.com/open-research/open-access/for-authors/waivers-and-discounts.html. All articles in this Special Issue will be published without cost to the contributing authors. The deadline for submitting manuscripts for in this Special Issue is the 28th of February 2022. Please remember to select “yes” when asked if you are submitting a manuscript to be considered as part of the Special Issue. Please email [email protected] or the Editor-In-Chief ([email protected]) if you would like to receive more details. We are excited to launch this Special Issue, and we look forward to receiving your 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".