The priority of prediction in ecological understanding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of science is to understand the natural world; we argue that prediction is the only way to demonstrate scientific understanding, implying that prediction should be a fundamental aspect of all scientific disciplines. Reproducibility is an essential requirement of good science and arises from the ability to develop models that make accurate predictions on new data. Ecology, however, with a few exceptions, has abandoned prediction as a central focus and faces its own crisis of reproducibility. Models are where ecological understanding is stored and they are the source of all predictions – no prediction is possible without a model of the world. Models can be improved in three ways: model variables, functional relationships among dependent and independent variables, and in parameter estimates. Ecologists rarely test to assess whether new models have made advances by identifying new and important variables, elucidating functional relationships, or improving parameter estimates. Without these tests it is difficult to know if we understand more today than we did yesterday. A new commitment to prediction in ecology would lead to, among other things, more mature (i.e. quantitative) hypotheses, prioritization of modeling techniques that are more appropriate for prediction (e.g. using continuous independent variables rather than categorical) and, ultimately, advancement towards a more general understanding of the natural world. Synthesis Ecology, with a few exceptions, has abandoned prediction and therefore the ability to demonstrate understanding. Here we address how this has inhibited progress in ecology and explore how a renewed focus on prediction would benefit ecologists. The lack of emphasis on prediction has resulted in a discipline that tests qualitative, imprecise hypotheses with little concern for whether the results are generalizable beyond where and when the data were collected. A renewed commitment to prediction would allow ecologists to address critical questions about the generalizability of our results and the progress we are making towards understanding the natural world.
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.
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) | 0.020 | 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