Recruitment dynamics of Hiatella arctica within a high Arctic site (Young Sound Fjord, NE Greenland)
Bibliographic record
Abstract
Recruitment dynamics including what determines the success or failure of Arctic benthic invertebrates are poorly known despite their important role for population dynamics. The main objective of this study was to assess the potential influence of extreme physical constraints related to freshwater discharge on the recruitment of a dominant bivalve Hiatella arctica within a High Arctic fjord (Young Sound, NE Greenland). We collected young recruits over several sampling periods from 2016 to 2018 at two contrasting sites (inner vs. middle fjord) for 5-weeks to 12-months and measured their abundance, size at metamorphosis and lipid class composition. Young stages of H. arctica settled from June to the end of October, when trophic conditions are optimal. We hypothesize that growth stops during winter due to poor trophic conditions. Data suggest that abundance of recruits, their total lipid concentration and composition of lipid classes are similar at both sites. However, size classes were different with six separate cohorts detected at one station and one at the inner station, which may be attributed to discrete spawning events and possible secondary migration. Based on an assessment of their potential age, we hypothesize that spat batches recruiting earlier in the summer exhibit better growth performance probably related to better food quality and quantity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".