Stand density and species richness affect carbon storage and net primary productivity in early and late successional temperate forests differently
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract How stand density and species richness affect carbon (C) storage and net primary productivity (NPP) changes with forest succession is poorly understood. We quantified the C storage of trees and the aboveground NPP in an early successional secondary birch forest (birch forest) and a late successional mixed broadleaf‐Korean pine ( Pinus koraiensis ) forest (mixed forest) in northeastern China. We found that: 1) tree C storage in the mixed forest (120.3 Mg C ha −1 ) was significantly higher than that in the birch forest (78.5 Mg C ha −1 ), whereas the aboveground NPP was not different between the two forest types; and 2) only stand density had a positive linear relationship with tree C storage and aboveground NPP in the birch forest. In the mixed forest, both tree C storage and aboveground NPP were significantly affected by the combination of the stand density and species richness. The tree C storage to stand density and species richness relationships were hump‐shaped. The aboveground NPP increased with increasing stand density, but its relationship to species richness was hump‐shaped. We conclude that the effect of stand density and species richness on tree C storage and aboveground NPP was influenced by forest stand succession, and such effects should be considered in studying stand density‐ and species richness‐ ecosystem function (e.g., C storage and NPP) relationships in temperate forest ecosystems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it