A bioclimatic model for the distribution of <i>Sphagnum</i>‐dominated peatlands in North America under present climatic conditions
Bibliographic record
Abstract
Abstract Aim A model was developed that estimates the climatic and geographical distribution of Sphagnum‐dominated peatlands in western Canada and North America. Location The model is based on the abundance of Sphagnum species on 640 peatland sites located in proximity to permanent weather stations throughout western Canada. Methods The per cent cover of Sphagnum species was quantified for all possible combinations of mean annual temperature (MAT) and mean annual total precipitation (MATP) found in western Canada. Abundance values were expanded to include all climatic values in North America, by interpolating within the matrix of values for western Canada using distance‐weighted means. Because Sphagnum‐dominated peatlands do not occur in areas having summer moisture index (P–PET) values ≤ 6 cm, that value was also included in the model. The result produced a matrix of 3360 grid nodes in which each node was characterized by MAT, MATP and P–PET values and the presence of Sphagnum‐dominated peatlands. The climatic data in the model was then match merged with the MAT, MATP and P–PET values obtained from Leemans & Cramer (The IIASA Database for Mean Monthly Values of Temperature, Precipitation, and Cloudiness on a Global Terrestrial Grid. International Institute for Systems Analysis, Laxenberg, 1991), which is a global data set that also contains geographical data on a 0.5° latitude by longitude grid. The geographical distribution of Sphagnum‐dominated peatlands was then mapped for western Canada and North America. Those maps were compared with the observed distribution of bogs and poor fens for the same area. Results Results of the comparisons indicated that the model's estimations matched the southern distribution of Sphagnum‐dominated peatlands relatively closely. Results for the northern distribution, however, were not as accurate. The overall accuracy was c. 80%. Main conclusions Some of the errors were caused by inaccurate climatic data which resulted from the lack of permanent weather stations in northern areas and the lack of definition of the northern limits of the actual distribution of bogs and poor fens. In areas where both the climatic data and the actual distribution of Sphagnum‐dominated peatlands are accurate, the model accuracy was c. 90%.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".