Hepatotoxicity Associated with Etanercept in Psoriatic Arthritis
Bibliographic record
Abstract
Abstract Climate change has induced pronounced shifts in the reproductive phenology of plants, with the timing of first flowering advancing in most species. Indeed, population persistence may be threatened by the inability to track climate change phenologically. Nevertheless, substantial variation exists in biological responses to climate change across taxa. Here, we explore the consequences of climate change for flowering phenology by integrating data from a long-term observational study and a manipulative experiment under contemporary conditions. Dissecting the environmental factors that influence phenological change will illuminate why interspecific variation exists in responses to climate change. We examine a 43-year record of first flowering for six species in subalpine meadows of Colorado in conjunction with a 3-year snow manipulation experiment on the perennial mustard Boechera stricta from the same site. We analyze shifts in the onset of flowering in relation to environmental drivers known to influence phenology: the timing of snowmelt, the accumulation of growing degree days, and photoperiod. At our study site, climate change is reducing snowpack and advancing the timing of spring snowmelt. We found that variation in phenological responses to climate change depended on the sequence in which species flowered, with early-flowering species flowering faster, at a lower heat sum, and under increasingly disparate photoperiods in comparison to species that flower later in the season. Furthermore, climate change is outpacing phenological change for all species. Early snow removal treatments confirm that the timing of snowmelt governs observed trends in flowering phenology of B. stricta and that climate change can reduce the probability of flowering, thereby depressing fitness. Shorter-term studies would not have captured the trends that we document in our observational and experimental datasets. Accurate predictions of the biological responses to climate change require a thorough understanding of the specific environmental factors driving shifts in phenology.
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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) | 0.002 | 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".