Interannual variability in a plankton time series
Bibliographic record
Abstract
Abstract Temporal changes in a plankton time series are examined, with an emphasis on interannual variability. A stochastic cycle model is used which describes an annual cycle with a fixed frequency, but a randomly varying amplitude and phase. A state space representation is used with the Kalman filter, and associated fixed‐interval smoother, to provide estimation of the time‐varying state. Parameter estimation relies on maximum likelihood methods. A data set is considered comprised of an irregularly sampled time series of plankton (dinoflagellate) abundance over a 12 year period in the Bay of Fundy, off the east coast of Canada. Analysis of the log10‐transformed data indicated timing changes in the seasonal cycle of up to 23 days. Significant variations in abundance relative to the mean cycle were found for some highly sampled summer periods. Case deletion diagnostics identified two influential observations, one of which has a large impact on the estimated system noise. Examination of the sampling protocol, or monitoring design, indicates the need to reduce the observation error variance in order to improve detection of interannual variations in plankton abundance. Copyright © 2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".