REGULAR ARTICLE Bryophyte-cyanobacterial associations as a key factor in N2-fixation across the Canadian Arctic
Bibliographic record
Abstract
Abstract Nitrogen inputs via biological N2-fixation are important in arctic environments where N often limits plant productivity. An understanding of the direct and indirect theoretical causal relationships between key intercorrelated variables that drive the process of N2-fixation is essential to understanding N input. An exploratory multi-group Structural Equation Modeling (SEM) approach was used to examine the direct and indirect effects of soil moisture, plant community functional composition, and bryophyte and lichen abundance on rates of nitrogen fixation at a low arctic ecosystem, two high arctic oases and a high arctic polar desert in the Canadian Arctic. Increasing soil moisture was strongly associated with an increas-ing presence of bryophytes and increasing bryophyte abundance was a major factor determining higher N2-fixation rates at all sites. Shrubs had a negative effect on bryophyte abundance at all sites with the exception of the polar desert site at Alexandra Fjord highland. The importance of competition from vascular plants appears to be greater in more productive sites and may increase at lower latitudes. Moisture availability may have an indirect effect on ecosystem develop-ment by affecting N input into the system with bryophyte-cyanobacterial associations playing an im-portant intermediary role in the process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".