Corrigendum: Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
Bibliographic record
Abstract
The dataset referred to as the IPPN dataset (p. 4) in the original article is now referred to by its authors as the PRL dataset. This dataset was used because it includes annotations for all three of the tasks performed in the validation experiments. In the results on the leaf counting task (Table 2), the proposed method was compared against two results from the literature. However, the results reported in the cited papers were performed on a different version of the dataset, which is referred to by its authors as CVPPP 2015_LCC (for the leaf counting competition of the 2015 Computer Vision Problems in Plant Phenotyping workshop). Therefore, the direct comparison is not warranted. However, R^2 between the actual and predicted leaf counts for the Plant/Ara2012, Plant/Ara2013-Canon, and Plant/Tobacco leaf counting datasets as presented in the article are 0.85, 0.90, and 0.74, respectively, demonstrating strong performance without the context of this comparison. This error does not change the scientific conclusions of the article in any way.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.171 | 0.105 |
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".