Contrasting total carbon stocks between ecological site series in a subboreal spruce research forest in central British Columbia
Bibliographic record
Abstract
A study was conducted to determine if consideration of ecological site classification in combination with stand age would describe total ecosystem carbon (C) better than consideration of just stand age alone. The research was conducted in the 9250 ha University of Northern British Columbia/The University of British Columbia Aleza Lake Research Forest in central British Columbia. Over three field seasons (2003–2005), 38, 72, and 27 plots were established in mesic, subhygric, and hygric stands, respectively, with stand ages ranging from 5 to 350+ years. Mineral soil C stocks were significantly influenced by moisture regime, where hygric > subhygric > mesic (93, 77, and 65 t C·ha–1, respectively). Mineral soil and forest floor C stocks were not related to stand age, indicating their resilience to partial-cut and clear-cut forest harvesting systems historically implemented throughout the study area. Subhygric stands had the highest total ecosystem C stocks in the Aleza Lake Research Forest, having approximately 18% more C than mesic and hygric stands, principally due to higher mineral soil C stocks (than mesic stands) and improved C sequestration in large trees (over hygric stands). Consideration of ecological site classification in addition to stand age information improved total ecosystem C stock estimates over the use of stand age alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".