Extraction of interannual trends in seasonal events for ecological time series
Bibliographic record
Abstract
The statistical analysis of environmental monitoring data is an important issue in detecting year‐to‐year changes in levels and timings of important ecological events. In many cases, this trend detection must explicitly view interannual changes from the context of an evolving seasonal cycle. This study analyses weekly sampling data from a long‐term ocean monitoring program near Halifax, Nova Scotia, Canada. A state space model of evolving seasonality with a quadratic trend is fit to these time series to extract the signal from the noisy and irregularly sampled data. The procedure uses Kalman filter innovations to estimate model parameters. A fixed interval smoother is then applied to estimate the system state. The resultant state estimates are subjected to a trend analysis carried out with respect to key ecological events: the level and timing of the peak, trough, spring, and fall abundances. These events are identified using derivative information, followed by a regression‐based trend analysis. The analysis found a number of significant linear trends in the biogeochemical variables considered. More generally, the approach used here is suitable for use with monitoring data exhibiting a unimodal seasonal signal with noise and missing values.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".