Machine Learning in Environmental and Climate Science: Overview and Introduction
Bibliographic record
Abstract
Abstract Machine learning (ML), a major branch of artificial intelligence, has been advancing environmental science beyond what is possible with the traditional approaches of physics, chemistry, biology, and statistics. ML and statistics are both data science approaches; however, relative to statistics, ML trades off interpretability for prediction accuracy. Poor interpretability initially hindered the acceptance of ML methods in environmental science. ML methods are now widely used in the fields of atmospheric science, oceanography, cryospheric science, hydrology, forestry, agricultural science, and climate science. The most common ML methods are neural network (NN) models, inspired by biological NNs in animal brains. Deep learning, that is, deep NN models, has become prominent since the mid-2010s, with the number of layers of mapping in deep NN models being much larger than in the earlier NN models. ML methods were initially introduced into environmental science as nonlinear statistical tools, with no direct relation to numerical models based on physics (“physics” being used in the broadest sense, i.e., physics + chemistry + biology, etc.). The recent merging of the two entirely different approaches, ML and numerical modeling, points to a new future for environmental and climate science.
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How this classification was reachedexpand
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".