Demography of three dominant sedges under contrasting grazing regimes in the High Arctic
Bibliographic record
Abstract
Abstract. Tiller demography of Carex aquatilis ssp. stans, Carex membranacea, and Eriophorum angustifolium ssp. triste was investigated in ungrazed and grazed high arctic vegetation on central Ellesmere Island, Canada. Tiller birth, growth, flowering and death were studied from excavated clonal fragments, and tiller density and biomass were studied from excavated turfs. Five life‐cycle stages were determined: dormant buds, juvenile, mature, flowering and dead tillers. A stage‐based transition matrix model was developed to estimate the long‐term dynamics of the sedge populations and to compare life‐history strategies between ungrazed and grazed populations. Short‐term and retrospective models, based on the growth during the sampling year and during the lifetime of the clonal fragments, respectively, were compared to see how well the short‐term model can describe demography of long‐lived plants. According to the short‐term model, tiller populations were decreasing (λ < 1 except for C. membranacea), whereas the retrospective model indicated that the tiller populations were increasing. Tiller population growth rates did not differ between ungrazed and grazed habitats. Nevertheless, the similar growth rates may be obtained by balanced differences in the vital rates between plants of the two habitats. The plants in the ungrazed habitat tended to remain in their current life‐cycle stage, whereas plants in the grazed habitat moved quickly to the next stage and died earlier. C. aquatilis ssp. stans appears to gain a competitive advantage over the other species under intensive grazing, as indicated by the higher tiller density and greater below‐ground biomass in grazed vegetation. The greater amount of below‐ground biomass apparently buffers C. aquatilis ssp. stans against grazing better than the other species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".