Phenology metrics for ocean waters with application to future climate change in the Northwest Atlantic Ocean
Bibliographic record
Abstract
Phenology metrics quantify the timing of seasonal events; future climate projections of changes to these metrics can be used in long-term ecosystem-based approaches to ocean resource management. Here a set of phenology metrics for ocean waters is presented. These metrics include three common ones: the onset of spring, the length of the growing season, and the onset of stratification. In addition, five novel metrics have been derived, including two that are based on the duration of thermal stress, defined as the amount of time that the future climate spends above the present climate maximum temperature; two that provide pelagic and demersal development indices by measuring the difference in time for a given number of present climate surface or bottom temperature degree days to arrive in the future; and a fifth metric that represents the absolute difference in a scalar quantity between the future and present climates. Spatial maps of the changes in these metrics for the mid-21st century have been derived from a high-resolution simulation of the Northwest Atlantic Ocean. A focus of this study was the application of the metrics to predict changes in ecosystem components in the future. Eight applications are presented for the Northwest Atlantic Ocean shelf region, describing predictions of shifts in the timing of inshore lobster migration; increased mortality, earlier spawning times and increased length-at-age for cod; reduced egg development times for shrimp; thermal stress on herring; and changes in habitat conditions for halibut and snow crab. This set of phenology change metrics serves as a starting point to illustrate the diverse ecosystem-related calculations possible using future climate ocean model output.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".