Surrogates Underpin Ecological Understanding and Practice
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
A surrogate is a proxy measure for an attribute of true interest that is too difficult or costly to measure directly. Surrogacy is widely used in the environmental sciences, as well as in other disciplines such as clinical medicine and pharmacology (Lindenmayer et al. 2015a; Barton et al. 2015). In medicine, for example, easily-quantified properties of blood (such as cholesterol level) are regularly used to infer a patient’s health, risk of disease, or response to a medical treatment (Barton et al. 2015). Similarly, ecologists often monitor attributes such as carbon stocks, species richness or vegetation structure to infer the overall state of biodiversity, risk of undesired change, or response to a management intervention (Lindenmayer et al. 2015a). \n \nSurrogates are often used in applied ecology to inform decisions about biodiversity management, atmospheric pollution and conservation reserve selection (e.g. Rodrigues and Brooks 2007). However, proxy measures are also used widely in fundamental ecology. Ecosystem properties like productivity, fire severity and water quality are almost exclusively inferred from related but indirect measures (e.g. Keeley 2009). This implicit use of surrogacy is often not acknowledged outside of the applied disciplines. The conceptual and analytical frameworks developed to improve surrogacy in applied contexts therefore have much to offer research in fundamental ecology. Similarly, the causal frameworks and search for mechanism in fundamental ecology has much to offer applied surrogacy. In our view, integrating and communicating the lessons from each will lead to better outcomes for both. \n \nHere we consider how fundamental tenets from surrogate research, particularly those that deal with intrinsic uncertainty and risk, are underappreciated in broader ecological research. Our assertion is that explicit recognition of the use of surrogates will benefit all ecological research through improved evaluation of the accuracy, consistency and certainty of the inferences drawn from measures, regardless of the context.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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