Flower phenological events and duration pattern is influenced by temperature and elevation in Dhauladhar mountain range of Lesser Himalaya
Bibliographic record
Abstract
Studying phenology is undeniably one of the most effective ways to monitor and perceive how a particular plant species interact and respond to varying environmental conditions. In this study, the shifts in flowering phenological events of 24 herbaceous species with the changing elevation and temperature were observed in Dhauladhar Mountain range of Lesser Himalaya. Five permanent plots (20 × 20 m2) were established from 2,000 m to 4,000 m elevation at each rising 500 m distance for the documentation of herbaceous flora. Abundance-based documentation of flowering phenological events (onset of flowering duration [OFD] ≥ 10% to 30%, the peak of flowering duration [PFD] > 30% to 80%, the end of flowering duration [EFD] > 80% and total flowering duration [FD] as the difference of end and onset of flowering DOY) was done in each plot. The onset, the peak, and the end of the flowering day of the year (DOY) were recorded at 20%, 55%, and 90% floral abundance, respectively, from 1st of January. Upon analyses, the Linear-mixed effects model depicted significant differences in the flowering phenological events with elevation and soil temperature. The onset of flowering DOY and peak of the flowering DOY varied significantly with soil temperature gradient, while the end of flowering DOY showed non-significant interaction. The linear model of ANOVA demonstrated that OFD, PFD, EFD and FD varied significantly with elevation at p < 0.001. FD increased with elevation for most of the species, whereas two species, Aquilegia pubiflora and Primula denticulata, showed early incidences of flowering compared to other species. Temperature played an imperative role in influencing the day of initiation and duration of these phenological events. High plasticity of flowering phenological events is advantageous in high elevation regions where pollinators are scarcely present. Therefore, documenting shifting incidences of flowering phenological events would help the researcher to effectively predict climate change effects on alpine communities in near future.
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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 | 0.001 |
| 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) | 0.001 | 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".