Establishing climate–growth relationships for yelloweye rockfish (<i>Sebastes ruberrimus</i>) in the northeast Pacific using a dendrochronological approach
Bibliographic record
Abstract
Abstract We applied dendrochronology (tree‐ring) methods to develop multidecadal growth chronologies from the increment widths of yelloweye rockfish (Sebastes ruberrimus) otoliths. Chronologies were developed for the central California coast, a site just north of Vancouver Island, British Columbia, and at Bowie Seamount west of the Queen Charlotte Islands, British Columbia. At each site, synchronous growth patterns were matched among otoliths via the process of cross‐dating, ensuring that the correct calendar year was assigned to all increments. Each time series of growth‐increment measurements was divided by the values predicted by a best‐fit negative exponential function, thereby removing age‐related trends. These detrended time series were averaged into a master chronology for each site, and chronologies were correlated with monthly averages of sea surface temperatures, upwelling, the Northern Oscillation Index, and the Pacific Decadal Oscillation. The two northern growth chronologies positively correlated with indices of warm ocean conditions, especially from the prior summer through the spring of the current year. During the same period, the California chronology positively correlated with indices of cool ocean conditions, indicating an opposing productivity regime for yelloweye rockfish between the California Current and the Gulf of Alaska. Overall, this study demonstrates how tree‐ring techniques can be applied to quickly develop annually resolved chronologies and establish climate–growth relationships across various temporal and spatial scales.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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 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".