Seasonal variation in<sup>15</sup>N natural abundance in subarctic plants of different life-forms
Bibliographic record
Abstract
Plants can be expected to utilize different sources of nitrogen with different proportions of 15N at different times of the year. We expected this to be reflected in a seasonal variation in the natural abundance of plant 15N, and that this pattern would vary among life-forms or species. To test this hypothesis, we studied the δ15N of eight different life-forms, selecting two representatives from each of four categories (woody deciduous, woody evergreen, graminoid, and cryptogam life-forms) at two locations having different levels of precipitation, over a six-month period. Sampling was conducted in mid-winter, during snowmelt in May, after leaf emergence, in mid-August, and in September. The sampled species showed a highly significant seasonal pattern in the natural abundance of 15N. Within each species and site, the δ15N showed a difference on average of 3.6% (range from 2.1 to 5.3%) between minimum and maximum over the sampling period. In most cases δ15N was highest in mid-winter and lowest at start of the growing season. Most species studied showed some common trends: (i) a decline in δ15N from mid-winter to pre-snowmelt (May); (ii) an increase from snowmelt to mid-June (mainly in plants sampled at one site); and (iii) a late-season decline in δ15N (August to September). Life-forms differed from each other in terms of their pattern of seasonal variation (harvest × life-form interaction) and between sites (site × life-form interaction). Thus, the outcome of comparisons of natural δ15N within and among species or sites depends on the time of year of sampling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".