Making predictive modelling ART: accurate, reliable, and transparent
Bibliographic record
Abstract
Abstract Models are increasingly being used for prediction in ecological research. The ability to generate accurate and robust predictions is necessary to help respond to ecosystem change and to further scientific research. Successful predictive models are typically accurate, reliable, and transparent regarding their assumptions and expectations, indicating high predictive capacity, robustness, and clarity in their objectives and standards. Research on improving these properties is becoming more common, but often individual research projects are focused on a single aspect of the modelling process and are typically disseminated only within the field where the research originated. The goal of this review is to synthesize research from various disciplines and topics to provide a coherent framework for developing efficient predictive models. Our framework summarizes the process of creating predictive models into three main stages: (1) Framing the Question; (2) Model‐Building and Testing; and (3) Uncertainty Evaluation with proposed strategies associated with each stage to help produce more successful predictive models. The key strategies identified within our framework form specific guidelines, providing a new perspective to help researchers make predictive modelling more accurate, reliable, and transparent.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.098 | 0.003 |
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".