Modelling moss-derived carbon in upland black spruce forests
Bibliographic record
Abstract
Mosses play a key role in the carbon (C) cycle of upland black spruce (Picea mariana (Mill.) BSP) forests; however, national reporting models such as the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) do not include mosses. This study examined whether widely available plot-level merchantable tree volume could predict, for black spruce ecosystems in Canada’s boreal forest, the relative proportions of sphagnum and feather moss ground cover and moss net primary productivity (NPP). A field study found that merchantable tree volume was significantly related to tree canopy openness (R2 = 0.61, P < 0.001), which could then be used to model the relative ground cover of feather moss (R2 = 0.5, P < 0.001) and sphagnum (R2 = 0.45, P < 0.001) and NPP of feather moss (R2 = 0.41, P < 0.001) and sphagnum (R2 = 0.28, P < 0.001). The resulting MOSS-C submodel increased the accuracy of the CBM-CFS3’s prediction of organic-horizon C five-fold and could explain large-scale variation in sites dominated by sphagnum with large organic-layer C pools but not fine-scale variation in dryer sites. To improve MOSS-C accuracy, future studies should focus on varying decomposition and fire regime parameters based on regional climate or plot-level vegetation parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".