Publisher Correction: Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference
Bibliographic record
Abstract
The original version of this Article contained errors in the Methods section ‘Target causal effect’, in which terms were omitted from the mathematical definitions of the causal effect and average causal effect. These sentences incorrectly read “The causal effect of a change in richness from R ′ to R ″ on productivity P in plot i is defined as [( R ″) − ( R ′)], where P i ( R ″) is the potential productivity outcome when R = R ″ and P ( R ′) is the potential productivity outcome when R = R ′ ( R ′ ≠ R ″).” and “The average causal effect of a change in biodiversity from R ′ to R ″ across all plots is [( R ″) − P ( R ′)], where E [·] is the expectation operator.”. The correct version states “[ P i ( R ′′) − P i ( R ′)]” in place of “[(R″) − (R′)]”, “ P i ( R ′)” in place of “ P ( R ′)”, and “ E [ P i ( R ′′) − P i ( R ′)]” in place of “[(R″) − P(R′)]”. This has been corrected in both the PDF and HTML versions of the Article.
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 | 0.020 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.049 | 0.024 |
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".