Bibliographic record
Abstract
In this final chapter, we survey the applications of machine learning methods in the various areas of environmental sciences – e.g. remote sensing, oceanography, atmospheric science, hydrology and ecology. More examples of applications are given in Haupt et al . (2009). In the early applications of methods like NN to environmental problems, researchers often did not fully understand the problem of overfitting, and the need to validate using as much independent data as possible, by e.g. using crossvalidation. For historical reasons, some of these early papers are still cited here, although the control of overfitting and/or the validation process may seem primitive by latter day standards. In some of the papers, the authors used more than one type of machine learning method and compared the performance of the various methods. A word of caution is needed here. In our present peer-reviewed publication system, a new method can be published only if it is shown to be better in some way than established methods. Authors therefore tend to present their new methods in the best possible light to enhance their chance of passing the peer review. For instance, an author might test his new method A against the traditional method B on two different datasets and find method A and B each outperformed the other on one dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.043 |
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".