Corrigendum: Age effects of Moso bamboo on leaf isoprene emission characteristics
Bibliographic record
Abstract
Incorrect Funding Please correct "Grant No. C02005" to "Grant No. 2021C02005"," Grant No. 2045210652" to " Grant No. 32071731" and " A∓F " to "A&F"]. The correct Funding statement appears below.FUNDING[This study was funded by the Key Research and Development Program of Zhejiang Province (Grant No. 2021C02005); Lishui Key Scientific and Technological Innovation Team (Grant No.2018cxtd02); the National Nature Science Foundation of China (Grant No. 32071731, 32001102); Scientific Research Foundation of Zhejiang A&F University (Grant No. 2020FR050); Scientific Research Foundation of Jiyang College of Zhejiang A&F University (Grant No. 05251700038); the Overseas Expertise Introduction Project for Discipline Innovation (111 Project D18008)]The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.068 |
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".