Multi-year carbon dioxide flux simulations for mature Canadian black spruce forests and ombrotrophic bogs using Forest-DNDC
Bibliographic record
Abstract
Ecosystem modelling is a useful tool for gaining insight and quantifying the carbon exchange between the atmosphere and terrestrial ecosystems.This study examines how well Forest-DNDC (a process-based biogeochemical model for forests/wetlands) estimates carbon dioxide (CO 2 ) fluxes from Canadian boreal forests and peatlands.We also evaluate the appropriateness in using Forest-DNDC to establish the baseline conditions of CO 2 fluxes before land-use change.Two mature black spruce forests and two ombrotrophic bogs were selected for comparisons between modelled and measured CO 2 fluxes.Two vegetation parameters in Forest-DNDC were optimized, and a hydrologic parameter was calibrated for the CO 2 flux simulations.The daily GPP (gross primary production) and ER (ecosystem respiration) simulations from all the study sites were in close agreement with the observations (r 2 for GPP and ER equal 0.79-0.86 and 0.86-0.87,respectively).The results of this study show that Forest-DNDC is useful in establishing baseline exchanges for boreal ecosystems prior to land-use change.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".