Corrigendum to “A Modified Climate‐Smart Push‐Pull Technology for the Management of Fall Armyworm ( <i>Spodoptera frugiperda</i> ) in the Semiarid Lands of Kenya”
Bibliographic record
Abstract
C. K. Mumo, P. W. Muturi, and B. M. Gichimu, “A Modified Climate-Smart Push-Pull Technology for the Management of Fall Armyworm (Spodoptera frugiperda) in the Semiarid Lands of Kenya,” International Journal of Agronomy 2024 (2024): 8038142, https://doi.org/10.1155/2024/8038142. In the article titled “A Modified Climate-Smart Push-Pull Technology for the Management of Fall Armyworm (Spodoptera frugiperda) in the Semiarid Lands of Kenya,” the information was omitted in the Acknowledgments section. The corrected section appears below: The work was carried out with the aid of a grant from UNESCO and the International Development Research Centre, Ottawa, Canada. The views expressed herein do not necessarily represent those of UNESCO, IDRC or its Board of Governors. The authors are grateful to the management of Catholic Diocesan Farm in Makima, through the Priest in charge (Fr. Paul Mutunga), for hosting the experiment. We apologize for this error.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.141 | 0.074 |
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".