The hidden season: growing season is 50% longer below than above ground along an arctic elevation gradient
Bibliographic record
Abstract
There is compelling evidence from experiments and observations that climate warming prolongs the growing season in arctic regions. Until now, the start, peak, and end of the growing season, which are used to model influences of vegetation on biogeochemical cycles, were commonly quantified using above-ground phenological data. Yet, over 80% of the plant biomass in arctic regions can be below ground, and the timing of root growth affects biogeochemical processes by influencing plant water and nutrient uptake, soil carbon input and microbial activity. We measured timing of above- and below-ground production in three plant communities along an arctic elevation gradient over two growing seasons. Below-ground production peaked later in the season and was more temporally uniform than above-ground production. Most importantly, the growing season continued c. 50% longer below than above ground. Our results strongly suggest that traditional above-ground estimates of phenology in arctic regions, including remotely sensed information, are not as complete a representation of whole-plant production intensity or duration, as studies that include root phenology. We therefore argue for explicit consideration of root phenology in studies of carbon and nutrient cycling, in terrestrial biosphere models, and scenarios of how arctic ecosystems will respond to climate warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".