Pigment signatures of phytoplankton communities in the Beaufort Sea
Bibliographic record
Abstract
Abstract. Phytoplankton are expected to respond to recent environmental changes of the Arctic Ocean. In terms of bottom-up control, modifying the phytoplankton distribution will ultimately affect the entire food web and carbon export. However, detecting and quantifying changes in phytoplankton communities in the Arctic Ocean remains difficult because of the lack of data and the inconsistent identification methods used. Based on pigment and microscopy data sampled in the Beaufort Sea during summer 2009, we optimized the chemotaxonomic tool CHEMTAX (CHEMical TAXonomy) for the assessment of phytoplankton community composition in an Arctic setting. The geographical distribution of the main phytoplankton groups was determined with clustering methods. Four phytoplankton assemblages were determined and related to bathymetry, nutrients and light availability. Surface waters across the whole survey region were dominated by prasinophytes and chlorophytes, whereas the subsurface chlorophyll maximum was dominated by the centric diatoms Chaetoceros socialis on the shelf and by two populations of nanoflagellates in the deep basin. Microscopic counts showed a high contribution of the heterotrophic dinoflagellates Gymnodinium and Gyrodinium spp. to total carbon biomass, suggesting high grazing activity at this time of the year. However, CHEMTAX was unable to detect these dinoflagellates because they lack peridinin. In heterotrophic dinoflagellates, the inclusion of the pigments of their prey potentially leads to incorrect group assignments and some misinterpretation of CHEMTAX. Thanks to the high reproducibility of pigment analysis, our results can serve as a baseline to assess change and spatial or temporal variability in several phytoplankton populations that are not affected by these misinterpretations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".