Temperature-based spawning habitat selection by capelin (Mallotus villosus) in Newfoundland
Bibliographic record
Abstract
Abstract The location of reproduction is a key life history trait, as it influences the bio-physical conditions that offspring experience and, thus, fitness. Capelin Mallotus villosus is a small (< 200 mm), short-lived forage fish that spawns in two habitats in coastal Newfoundland: warm beaches and cool, deep water (15–40 m). From 2009 to 2014, we investigated temperature-dependent spawning habitat use by quantifying hourly temperature at spawning sites during July within each habitat along with population-level spawning site use. Capelin did not spawn at sites with temperatures <2 °C or consistently >12 °C, supporting the estimated suitable spawning temperature range (2–12 °C). Spawning typically occurred exclusively at beaches early in July when deep-water habitat was too cold (i.e. <2 °C), and then switched to deep-water habitat later in July when beaches became too warm (i.e. >12 °C). Spawning overlapped for 1–3 d in both habitats when temperatures were within suitable ranges (2011, 2013), but capelin also spawned exclusively in one habitat when temperatures remained suitable in the other. The latter suggests that other factors influence spawning habitat selection, such as conspecific egg densities and other environmental conditions. Overall, the flexible use of spawning habitats, along with wide tolerance ranges of capelin eggs to environmental conditions, are likely key adaptations to maximize fitness and stabilize population dynamics of this important forage fish species in an unpredictable environment.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".