Multivariate control of heterotrophic bacterial abundance and zooplankton grazing in Labrador fjords (northeastern Canada)
Bibliographic record
Abstract
This study was conducted in 4 Labrador fjords (Nachvak, Saglek, Okak, and Anaktalak) during the summers of 2007 and 2013, early fall 2010, and late fall 2009. Our results show that water temperature combined with the availability of nutrients and organic substrates are the main abiotic factors controlling the abundance of heterotrophic bacteria in Labrador fjords. Bacterivory also played a crucial role, with heterotrophic bacteria exerting a significant bottom-up control on the abundance of heterotrophic nanoflagellates (r = 0.35, p < 0.05) and ciliates (r = 0.70, p < 0.01). During summer 2013, the intrinsic phytoplankton growth rate varied between <0 and 0.64 d-1, with a mean value of 0.36 d-1. The herbivory rate was highly variable, ranging from 0.01 to 0.86 d-1, with a mean value of 0.31 d-1. Grazing mortality was 6-fold higher than phytoplankton growth rate. Mean phytoplankton growth and herbivory rates in Labrador fjords were comparable to the Barents and Bering seas. The intrinsic growth rate of total heterotrophic bacteria ranged between <0 and 0.68 d-1, with a mean value of 0.30 d-1. Bacterivory varied from 0.01 to 0.95 d-1, with a mean of 0.30 d-1. Mortality due to grazing was up to 2.3 times higher than total bacterial growth rate. This study improves our understanding of the factors influencing the dynamics of heterotrophic bacteria and indicates that herbivory and bacterivory exert substantial control on microbial communities in Labrador fjords.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".