Exploratory Data Analysis of Climatic Trends in the Koyna Biodiversity Hotspot
Bibliographic record
Abstract
In the context of climate change, the present study analysed significant shifts in climatic trends on the dataset available from 1981 to 2022 of the Koyna wildlife sanctuary, a UNESCO biodiversity hotspot.The analysis adopts correlation and heatmap methods, utilizing the Pearson coefficient for trend analysis of rainfall, temperature, relative humidity, and surface pressure, a key climatic variable affecting ecosystem climates.The selection of these methods allows handling multivariate analyses for a subtle understanding of the interplay between these variables over time.Findings revealed increasing trends in rainfall and temperature, consistent surface pressure patterns, and stable relative humidity.Notably, Relative Humidity displayed significant associations with rainfall, surface pressure, and maximum temperature, suggesting collective influences on its fluctuations.The pivotal year, 2019, marked increased weather dynamics, aligning with the positive phase of the Indian Ocean Dipole.The prolonged Indian summer monsoon since 2019, disrupting the October heat transition, poses challenges for ecosystems, and soil conditions.The observed climatic trends, particularly the increased rainfall and temperature, underscore the urgent need for adaptive biodiversity conservation and management strategies within the sanctuary possibly enhancing habitat connectivity to allow for species migration and rescue measures to combat the effects of prolonged wet & dry periods.These findings call for the sanctuary's management to incorporate climate trend insights into their conservation tactics, ensuring the sanctuary's diverse ecosystems and species are resilient in the face of evolving climate conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".