Plankton size spectra as an indicator of larval success in Pacific sardine (<scp><i>Sardinops sagax</i></scp>)
Bibliographic record
Abstract
Abstract Estimating demographic changes in a population requires the measurement of some minimal combination of several vital rates, including the flux of individuals into a population, the population growth rate, individual growth rates and mortality rates. For larval fishes, the ratio of instantaneous mortality to growth (i.e., their ‘recruitment potential’) has been used to make inferences of cohort trajectory where measures of population growth rates are not attainable. Attaining estimates of mortality and growth is an arduous task, and use of the recruitment potential metric has been limited. Here, we relate size spectra of the broader plankton community to the recruitment potential of simultaneously sampled larval Pacific sardines (Sardinops sagax), from three voyages off eastern Australia. As the size structure of a population is determined by the ratio of mortality to growth, and there is remarkable consistency in size spectra across ecosystems, we test the hypothesis that the recruitment potential of larval fish is reflected in community‐level measures of plankton size spectra. Contrary to expectations, results from this study demonstrate a negative relationship between the slope of the plankton size spectra and the recruitment potential of larval Pacific sardine. However, we also demonstrate several other stronger relationships between recruitment potential and physical oceanographic parameters. Together, results suggest plankton size spectra are unlikely to reflect recruitment potential directly. Incorporating some size‐based aspects of the plankton community into a broader modelling framework with a range of oceanographic parameters could further our ability to determine how larval success varies across a seascape.
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 | 0.000 |
| 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".