Food web characteristics of High-Arctic Greiner Lake near Cambridge Bay (Ikaluktutiak), Nunavut, Canada
Bibliographic record
Abstract
The Arctic biome is currently undergoing major modifications with climate change. Arctic freshwater ecosystems, as sentinel ecosystems, can record major climate changes before they affect the entire region. However, to fully evaluate the amplitude of changes in Arctic freshwaters, we need to be able to compare future measurements to baseline data. To date, the food web characteristics of lakes are poorly described in remoter Arctic areas of Canada. Greiner Lake is located in the High-Arctic near Cambridge Bay (Ikalutktutiak) on Victoria Island, Nunavut, Canada, and is an important site for Inuit communities as it has always been known to support high fish biomass. The Lake Greiner watershed also represents a key monitoring site for the recently open Canadian High-Arctic Research Station (CHARS). We provide here a summary of the food web characteristics for Greiner Lake including data on primary producers, littoral and pelagic consumers and apex fish predators. The data were collected during the summers of 2017, 2018 and 2019. Organisms were collected with different types of nets (gill nets, zooplankton nets and kick-nets) and treated on site at CHARS for further analyses. Samples for stable isotopes and fatty acid analyses were preserved at -20°C at CHARS. Samples for stable isotopes were then sent to the stable isotope laboratory of Environment and Climate Change Canada at the University of Saskatchewan. Samples for fatty acid analyses were extracted, analyzed and quantified at University of Quebec In Chicoutimi, Quebec, Canada. Zooplankton community samples were preserved with 4% formaldehyde and then identified using an inverted microscope and binocular. Length measurements of individuals were used to calculate the zooplankton biomass with length-weight relationships and aid in determination of species composition. Algae biomass production (benthic and pelagic) were calculated with 14C assimilation and subsequent measure in a scintillation counter. Bacterial production was measured via assimilation of tritiated leucine. Zooplankton production was estimated via the measurement of enzymes released in water by organisms. Finally, we describe the species composition of the fish community and use their stomach contents to infer diet. The data allowed us to characterize the food web structure and provide estimation of fatty acid origin and transfer within the food web. The baseline data provided will permit future estimates of the amplitude of changes in the region and aid in the prediction of possible trajectories of Arctic freshwaters under human-induced stress.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".