Detecting One-Hundred-Year Environmental Changes in Western China Using Seven-Year Repeat Photography
Bibliographic record
Abstract
Due to its diverse, wondrous plants and unique topography, Western China has drawn great attention from explorers and naturalists from the Western World. Among them, Ernest Henry Wilson (1876 -1930), known as 'Chinese' Wilson, travelled to Western China five times from 1899 to 1918. He took more than 1,000 photos during his travels. These valuable photos illustrated the natural and social environment of Western China a century ago. Since 1997, we had collected E.H. Wilson's old pictures, and then since 2004, along the expedition route of E.H. Wilson, we took 7 years to repeat photographing 250 of these old pictures. Comparing Wilson's photos with ours, we found an obvious warming trend over the 100 years, not only in specific areas but throughout the entire Western China. Such warming trend manifested in phenology changes, community shifts and melting snow in alpine mountains. In this study, we also noted remarkable vegetation changes. Out of 62 picture pairs were related to vegetation change, 39 indicated vegetation has changed to the better condition, 17 for degraded vegetation and six for no obvious change. Also in these photos at a century interval, we found not only rapid urbanization in Western China, but also the disappearance of traditional cultures. Through such comparisons, we should not only be amazed about the significant environmental changes through time in Western China, but also consider its implications for protecting environment while meeting the economic development beyond such changes.
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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 | 0.001 |
| 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".