Interpreting tree responses to thinning and fertilization using tree‐ring stable isotopes
Bibliographic record
Abstract
• Carbon sequestration has focused renewed interest in understanding how forest management affects forest carbon gain over timescales of decades, and yet details of the physiological mechanisms over decades are often lacking for understanding long-term growth responses to management. • Here, we examined tree-ring growth patterns and stable isotopes of cellulose (δ(13)C(cell) and δ(18)O(cell)) in a thinning and fertilization controlled experiment where growth increased substantially in response to treatments to elucidate physiological data and to test the dual isotope approach for uses in other locations. • δ(13)C(cell) and δ(18)O(cell) results indicated that fertilization caused an increase in intrinsic water-use efficiency through increases in photosynthesis (A) for the first 3 yr. The combination treatment caused a much larger increase in A and water-use efficiency. Only the thinning treatments showed consistent significant increases in δ(18)O(cell) above controls. Changes in canopy microclimate are the likely drivers for δ(18)O(cell) increases with decreases in relative humidity and increases in leaf temperature associated with thinning being the most probable causes. • Tree-ring isotopic records, particularly δ(13)C(cell), remain a viable way to reconstruct long-term physiological mechanisms affecting tree carbon gain in response to management and climate fluctuations.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".