Net ecosystem productivity of boreal jack pine stands regenerating from clearcutting under current and future climates
Bibliographic record
Abstract
Abstract Life cycle analysis of climate and disturbance effects on forest net ecosystem productivity (NEP) is necessary to assess changes in forest carbon (C) stocks under current or future climates. Ecosystem models used in such assessments need to undergo well‐constrained tests of their hypotheses for climate and disturbance effects on the processes that determine CO 2 exchange between forests and the atmosphere. We tested the ability of the model ecosys to simulate diurnal changes in CO 2 fluxes under changing air temperatures ( T a ) and soil water contents during forest regeneration with eddy covariance measurements over boreal jack pine ( Pinus banksiana ) stands along a postclearcut chronosequence. Model hypotheses for hydraulic and nutrient constraints on CO 2 fixation allowed ecosys to simulate the recovery of C cycling during the transition of boreal jack pine stands from C sources following clearcutting (NEP from −150 to −200 g C m −2 yr −1 ) to C sinks at maturity (NEP from 20 to 80 g C m −2 yr −1 ) with large interannual variability. Over a 126‐year logging cycle, annualized NEP, C harvest, and net biome productivity (NBP = NEP–harvest removals) of boreal jack pine averaged 47, 33 and 14 g C m −2 yr −1 . Under an IPCC SRES climate change scenario, rising T a exacerbated hydraulic constraints that adversely affected NEP of boreal jack pine after 75 years. These adverse effects were avoided in the model by replacing the boreal jack pine ecotype with one adapted to warmer T a . This replacement raised annualized NEP, C harvest, and NBP to 81, 56 and 25 g C m −2 yr −1 during a 126‐year logging cycle under the same climate change scenario.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".