Data for: Response of boreal plant communities and forest floor carbon fluxes to experimental nutrient additions
Bibliographic record
Abstract
This is the final dataset for the publication "Response of boreal plant communities and forest floor carbon fluxes to experimental nutrient additions" published in the peer-reviewed journal Ecosystems in 2024. This dataset includes three primary files: the plant community composition data the plant functional trait data the forest floor carbon flux and associated environmental variables data. Combined, the plant community composition data and plant functional trait data were used to calculate the community-weighted mean traits presented in the manuscript. These data explored changes in community-weighted mean traits of both moss and understory vascular plant communities as well as forest floor carbon flux with nutrient fertilization emulating nutrient increases following permafrost thaw in a boreal peatland (Scotty Creek Research Station, NT: 61°18′ N, 121°,18′ W). We had two sites in this boreal peatland: one with high canopy cover, and one with low canopy cover. We conducted an experiment whereby we set up 25, 0.5 m^2 quadrats at two sites within a boreal peatland (50 total quadrats). We had five different nutrient treatments with five replicates each: an unmanipulated control (no fertilizer or mechanical disturbance), a manipulated control (no fertilizer, with mechanical disturbance similar to the fertilized treatments), shallow fertilization (nutrient added at 20cm soil depth emulating increasing microbial mineralization with warming soil), deep fertilization (nutrient added at 40 cm soil depth emulating release of previously frozen nutrient-rich soil to the active layer), and shallow + deep fertilization (emulating both the shallow and deep treatments simultaneously). 12-4-8 NPK MiracleGro slow-release fertilizer was applied at a dosage of 6 g N m-2. This value is roughly four times that of the predicted maximum rate of increase in N from rising soil temperature and permafrost thaw (e.g., 1.3 g N m-2 yr-1; Keuper and others 2012, Global Change Biology 18: 1998) but is similar to Keuper and others (2017, Global Change Biology 23: 4257). Furthermore, being slow-release fertilizer, we expected slow nutrient release over the two-year experiment, mimicking natural conditions. The two sites whereby we set up the experiment differed in canopy cover and therefore allowed us to test if light availability effected the response of carbon fluxes and plant communities to nutrient increases. See (link to paper) for more detailed methodology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.069 |
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".