Does Tree Species Composition Affect Productivity in a Tropical Planted Forest?
Bibliographic record
Abstract
Regrettably, it has come to our attention that the recent paper ‘Does Tree Species Composition Affect Productivity in a Tropical Planted Forest?’ Biotropica 47(5): 559–568, 2015 contains some minor errors in the text, literature cited, and Table 2. These errors, corrected below, have no impact on the analysis or interpretation of the results. We regret any inconvenience this has caused. Page 561. The following sentence should read: Selected traits were: specific leaf area (SLA) and wood density measured in Sardinilla, and seed mass using data from the Royal Botanic Gardens Kew Seed Information Database (average dry weight) (Royal Botanic Gardens Kew, 2014) and the literature (Sautu et al. 2006). Table 2. Some values in the seed mass column were mistakenly rounded to the nearest whole number, but presented with two decimal places; four seed mass values were reported incorrectly. The following, now cited in both the text and Table 2, should be included in the literature cited.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".