A Record-Setting 2021 Heat Wave in Western Canada Had a Significant Temporary Impact on Greenness of the World’s Largest Protected Temperate Rainforest
Bibliographic record
Abstract
Extreme climate anomalies are expected to become more frequent under climate change, and rare extreme events, such as the 2021 western North American heat wave, provide an opportunity for comparative empirical analysis of ecosystem resilience. This study evaluates anomalies in a remotely sensed enhanced vegetation index (EVI) in the aftermath of the record-setting western North American heat wave in 2021, with temperatures approaching 50 °C in coastal and interior regions of the Pacific Northwest. The results show that the forest ecosystems most affected were not necessarily those that experienced the highest absolute temperature values. Instead, the greatest reductions in greenness were observed across northern coastal temperate rainforests. Most affected were the cooler, very wet, hyper-maritime ecosystems that are normally buffered from large temperature fluctuation by a strong oceanic influence. In contrast, moisture-limited forests of the interior plateau of British Columbia, where most of the all-time record temperatures occurred, generally showed normal or even increased productivity during and after the heat wave. A putative explanation for this heat resistance of interior forests was normal or above average precipitation leading up to the heat event, allowing for transpirational cooling. Nevertheless, the data suggest that the largest protected coastal temperate rainforest in the world, with 6.4 million hectares, is comparatively more vulnerable to extreme heat waves, which are expected to become more frequent under climate warming, than other ecosystems of the Pacific Northwest.
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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 | 0.001 | 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".