Size-based approaches to aquatic ecosystems and fisheries science: a symposium in honour of Rob Peters
Bibliographic record
Abstract
This special issue honours Rob Peters’ outstanding contributions to the field of aquatic ecology. It focuses on the size spectrum approach — in which individual organisms, rather than species, are the most basic biological unit — and highlights applications of this approach to fisheries management. The 21 papers in this issue cover three subject areas: (i) the use of size spectra to characterize variation in community structure, (ii) the development of size-based models of ecosystem dynamics to address fisheries questions, and (iii) applications of size-based theory to examine the consequences of variation in predator–prey size relationships, body size – trophic level relationships, and body size – life history relationships. The empirical studies herein demonstrate the utility of size spectra as indicators of population or community structure and for detecting impacts associated with environmental change. Future research focused on refining size-based sampling methods, standardizing metrics and analytical methods, understanding model sensitivity to the underlying assumptions, and comparative studies across ecosystems will enhance our ability to reliably interpret changes in size spectrum characteristics, thus facilitating their use as indicators of ecological change.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".