Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
Why this work is in the frame
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Bibliographic record
Abstract
Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CH 4 ) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH 4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH 4 ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CH 4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH 4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH 4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistence null model. Our forecasts with iterative model refitting estimated CH 4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 log e (mg CH 4 m −2 d −1 ) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CH 4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH 4 ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CH 4 ebullition predictions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it