Growth Rate of an Aquatic Bryophyte (<i>Warnstorfia fluitans</i> (Hedw.) Loeske) from a High Arctic Lake: Effect of Nutrient Concentration
Bibliographic record
Abstract
The High Arctic is one of the regions most susceptible to climate change on a global scale. Increased temperature, precipitation, and cloud cover are anticipated in the region, with consequent increases in nutrient runoff to surface waters. Mosses are often the dominant submerged macrophyte in Arctic and High Arctic lakes. If the growth rate of mosses in these lakes is nutrient-limited, then production could increase with climate changes that result in higher nutrient concentrations. We conducted a laboratory study to 1) measure the growth response of Warnstorfia fluitans (Hedw.) Loeske from a High Arctic lake to nitrogen and phosphorus availability; and 2) determine whether growth rate was N- or P-limited by examining its relationship to internal P and N content. The growth rates of W. fluitans were generally low, ranging from 0.003 to 0.012 day-1. The growth rates increased with increasing plant P content, but not with increasing N content, indicating that moss growth was P-limited at low P availability in the experiment. Critical plant P concentration for maximum growth rate was 0.086% dry weight. This is the first time a critical P threshold has been calculated. The results imply that if climate changes result in increased P concentrations in surface waters, a higher production of moss could occur in High Arctic lakes.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".