How do data collection and processing methods impact the accuracy of long‐term trend estimation in lake surface‐water temperatures?
Bibliographic record
Abstract
Abstract Identifying significant changes across lake ecosystems is important for understanding impacts of global environmental change. Synthesizing data on lake warming trends is challenging because individual lake datasets differ in the: (1) length of the time series available for analysis and (2) frequency of data collection (e.g., daily vs. monthly observations). This study aimed to address how dataset length, frequency of data collection, and strength of temperature trends could impact both the accuracy of summer surface‐water temperature trends and their statistical significance. Using Monte Carlo simulations, we found that accuracy in trend estimates and the ability to recover statistically significant trends were both directly related to trend strength, dataset length, and sampling frequency. To consistently retrieve statistically significant trend estimates that deviated < 25% from the true values, 30‐yr datasets with high warming rates (≥ 0.75°C decade−1) were required. These findings have important implications for efforts to analyze lake temperature trends, as the characteristics of many existing datasets fall within a range where our simulations predict low accuracy in trend estimates as well as a low probability of achieving statistical significance. Longer datasets are needed to accurately estimate warming trends and evaluate drivers of lake surface‐temperature changes, highlighting the need to support existing long‐term monitoring projects occurring across the globe, and to encourage updates to remotely sensed lake temperature datasets.
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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 | 0.138 | 0.397 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".