Temporal variation in fish egg and larval production by pelagic and bottom spawners in a large Newfoundland coastal embayment
Bibliographic record
Abstract
In highly seasonal environments such as coastal Newfoundland, local production, advection, and life history may influence ichthyoplankton community structure. The spring bloom occurs in cold water that slows development of eggs from pelagic spawners and may transport propagules from optimal nearshore areas before hatch. For bottom spawners that affix eggs to the bottom, the problem is reduced because only actively swimming larval stages are pelagic. We hypothesize that larvae of pelagic spawners are limited to warmer, summer waters, whereas larvae of bottom spawners are less constrained temporally and less subject to flushing from the nearshore environment. Ichthyoplankton taxa sampled in Placentia Bay, Newfoundland, during spring–summer in 1997–1999 showed consistent seasonal peaks in egg and larval abundance. Although pelagic egg production spanned spring and summer, larval abundance peaked late in summer or early fall in the most productive areas of the bay. Larval abundance of bottom spawners peaked in spring for most taxa. Thus, pelagic eggs hatch quickly in summer, and larvae can utilize the late peak in nearshore copepod abundance. Bottom spawners can utilize spring zooplankton because temperature-dependent development does not influence egg advection. Coastal advection and temperature influence how different life history groups exploit spatial and temporal peaks in production.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".