Reassessment of the life cycle of the pteropod Limacina helicina from a high resolution interannual time series in the temperate North Pacific
Bibliographic record
Abstract
Abstract Limacina helicina is the dominant pelagic gastropod mollusc species in temperate and polar ecosystems, where it contributes significantly to food webs and vertical flux. Currently, considerable uncertainty exists in the interpretation of L. helicina’s life cycle, hindering our understanding of its potential responses to environmental change. Here, we present size-frequency data on L. helicina collected from three consecutive years (2008–2010) in a North Pacific temperate fjord. Two methods of length-frequency analysis were used to infer the growth of L. helicina, i.e. linking successive means extracted from finite-mixture distributions, and using the ELEFAN software to fit seasonally oscillating versions of the von Bertalanffy growth equation to the available length-frequency data. Against a background of continuous low level spawning between spring and autumn, both approaches identified two sets of major cohorts, i.e. (i) spring cohorts (G1) spawned in March/April by (ii) overwintering cohorts (G). G overwintered with minimal to low growth, before undergoing rapid growth the following spring and completing the cycle by spawning the G1 generation and disappearing from the population by May/June. Our findings are discussed in the context of L. helicina response to climate change.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".