Accounting for the bin structure of data removes bias when fitting size spectra
Bibliographic record
Abstract
Size spectra are recommended tools for detecting the response of marine communities to fishing or to management measures. A size spectrum succinctly describes how a property, such as abundance or biomass, varies with body size in a community. Required data are often collected in binned form, such as numbers of individuals in 1 cm length bins. Numerous methods have been employed to fit size spectra, but most give biased estimates when tested on simulated data, and none account for the data’s bin structure (breakpoints of bins). Here, we used 8 methods to fit an annual size-spectrum exponent, b , to an example data set (30 yr of the North Sea International Bottom Trawl Survey). The methods gave conflicting conclusions regarding b declining (the size spectrum steepening) through time, and so any resulting advice to ecosystem managers will be highly dependent upon the method used. Using simulated data, we showed that ignoring the bin structure gives biased estimates of b , even for high-resolution data. However, our extended likelihood method, which explicitly accounts for the bin structure, accurately estimated b and its confidence intervals, even for coarsely collected data. We developed a novel visualisation method that accounts for the bin structure and associated uncertainty, provide recommendations concerning different data types and have created an R package (sizeSpectra) to reproduce all results and encourage use of our methods. This work is also relevant to wider applications where a power-law distribution (the underlying distribution for a size spectrum) is fitted to binned data.
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 | 0.001 |
| 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 | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".