A biophysical model of Calanus hyperboreus in the Gulf of St. Lawrence: Interannual variability in phenology and circulation drive the timing and location of right whale foraging habitat in spring and early summer
Bibliographic record
Abstract
Since 2015, North Atlantic right whale (NARW) occurrences have considerably increased in the Gulf of St. Lawrence (GSL), likely driven by a decrease in the availability of their historically preferred prey, the lipid-rich Calanus spp. (Copepoda), in their traditional foraging habitats. In the southern GSL (sGSL) where most NARW sightings occurred in recent years, the large bodied and lipid-rich Calanus hyperboreus dominates the Calanus spp. biomass in the spring and early summer, with important interannual variability hypothesized to be driven by variations in phenology and transport. This study examined the seasonal and interannual variability of phenology and relative distribution of C. hyperboreus biomass in the sGSL during the spring and early summer from 2016 to 2019, using a 3-D coupled biophysical model. We simulated the development and advection of the new cohort of C. hyperboreus from first copepodite (CI) stages in early spring to mid-July, a period during which they enter diapause as lipid-rich fourth copepodite (CIV) stages. Our study confirmed that C. hyperboreus distribution in the sGSL is mainly influenced by transport from adjacent deeper areas through interactions between ocean circulation and early copepodite stages during the spring. Model results indicated variable numbers of simulated individuals from the Laurentian Channel were transported annually to the sGSL during their active stages, entered diapause, and were retained near the seafloor in the summer. Biophysical simulation outputs could therefore contribute to seasonal-scale advice on locations of NARW potential foraging habitat formation and improve understanding NARW seasonal dynamics in the sGSL.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".