Size‐spectra analysis in the estuary: assessing fish nursery function across a habitat mosaic
Bibliographic record
Abstract
Abstract Estuaries act as nurseries for a wide variety of fish species, potentially providing vital foraging opportunities and refuge from predation for their juvenile residents. Yet, these dynamic environments are comprised of a mosaic of habitat types that span gradients of both salinity and physical habitat structure. Here, we present a novel use of size‐spectra analysis to infer nursery habitat function across the estuary habitat mosaic. Interpreting slope and intercept values of abundance against body mass size‐spectra regressions as indicators of predation risk and production, we constructed spectra for six distinct habitat types across the entire tidal influence of an unindustrialized estuary in coastal British Columbia. Based on catches of >200,000 individual fish representing 30 different species from April through September, the estuary rockweed mudflat habitat had the lowest size‐spectra slope and highest intercept, consistent with lower predation risk and higher production. Size‐spectra coefficients varied seasonally across the ecotone, indicating spatio‐temporal variation in key nursery functions. Size‐spectra can provide insight into key ecological processes of productivity and predation risk across dynamic aquatic habitats.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".