GAUSSIAN ERROR PROPAGATION APPLIED TO ECOLOGICAL DATA: POST‐ICE‐STORM‐DOWNED WOODY BIOMASS
Bibliographic record
Abstract
Error analysis using Gaussian error propagation (GEP) can be used to analytically determine the error or uncertainty produced by multiple and interacting measurements or variables. The technique is especially useful for studies that involve step‐by‐step calculations, where measurements taken at a smaller temporal or spatial scale are used to estimate a value at larger scales (e.g., daily total tree‐crown carbon assimilation is estimated from carbon assimilation rate per unit leaf area per unit time). The GEP technique is not well known and rarely used in ecology. The purpose of this paper is to illustrate the concepts and methods of GEP in a manner that is accessible and relevant to students and researchers in ecology. The technique is also extended to calculate the “error budget” and “sensitivity indices” of error sources. The concept of the “error structure” of an experiment or calculation is introduced, and different partitioning methods and optimization strategies for analyzing and reducing error, which further develop the potential usefulness of GEP, are shown. An example of its application to ecological data is demonstrated using the post‐ice‐storm‐downed woody‐biomass data set, previously reported by M. C. Hooper, K. Arii, and M. J. Lechowicz. Both the data and the error analysis can be viewed as being representative of and relevant to a general class of step‐by‐step and scaling‐up ecological calculations. Finally the use of GEP reveals that the error structure is a scale‐dependent quantity, a result that is relevant to both scaling theory and experimental design.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".