Bibliographic record
Abstract
Abstract A dilemma in ecology Ecologists aspire to build a discipline to both understand the natural world and to provide society with tools to make responsible decisions about the environment. For both of these purposes, most sciences have, at their core, a set of empirical generalizations that predict the behaviour of important properties of nature (e.g., Newton’s laws of mechanics, Mendeleev’s periodic table, Mendel’s genetics). Ecological science, in contrast, has favoured studies to understand processes (competition, population regulation, etc. – i.e., independent variables) rather than models that predict attributes of nature (dependent variables). Classical reductionist scientific training emphasizes studies of mechanisms under controlled experimental conditions. Yet, inferences about nature from experiments are nearly always unjustifiable extrapolations beyond the experimental conditions. Mechanisms that are statistically detectable in experimental systems may contribute very little to the variation of nature. Studies of ecological processes in isolation may contribute to “expert understanding,” but experts have been shown to be poor predictors of the behaviour of natural systems. A proposed solution The more relevant, often neglected question is: what factors can statistically account for the observed variance of nature? A more Newtonian approach to ecology would: (a) first, specify the properties of nature (i.e., dependent variables) whose variance is of concern; (b) develop models that statistically capture the variance of those properties in nature; (c) demonstrate that those models can predict independent data; and, only last, (d) experimentally test hypotheses about processes that could give rise to the predictable patterns in nature. Why it matters Successful disciplines identify specific goals and measure progress toward those goals. Predictive accuracy of properties of nature is a measure of that progress in ecology. Predictive accuracy is the objective evidence of understanding. It is the most useful tool that science can offer society.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".