Annual Scientific Meeting 2023 Conference Book of Abstracts
Bibliographic record
Abstract
Arctic char is an important country food for Aivilingmiutin Naujaat.The char overwinter in lakes, and once the ice breaks up they travel down rivers into coastal waters for their summer feeding phase.Naujaat fishers have noticed that the timing of char runs vary between rivers.The appearance of the fish also differs depending on where they are caught.To begin to understand how this phenology is linked to environmental conditions, we have investigated several rivers and lakes near Naujaat as a part of the ArcticNet IQP program.The team consists of Lead Coordinator and Field Technician, Johnny Tagornak, the Arviq HTO, field assistants from Naujaat, CEOS Coastal Oceanography Team at University of Manitoba, and Department of Fisheries and Oceans scientists, with financial support from Inuit Qaujisarnirmut Pilirijjutit (IQP).Here, we provide a summary and update on our findings.In order to capture the baseline conditions of the lake and ocean environments, water samples have been collected in winter, spring, and summer (2021-2023) for analysis of environmental properties such as nutrients, salinity, coloured dissolved organic matter (CDOM), trace elements, and oxygen isotope composition.Conductivity-temperature loggers were also deployed on moorings to capture baseline conditions of the lake, river, and marine environments over the annual cycle.From May-June 2022, ice-tethered mooring sensors successfully recorded the change in water temperature from winter to spring, but then had to be removed before ice-break up.Most recently, in May 2023, water samples were collected at five stations and returned to the University of Manitoba for analysis.During this time, we deployed bottom-mounted moorings through the sea ice that we plan to retrieve in August 2023 to capture the full winter to summer transition period that coincides with the char run.In this presentation we will provide an interpretation of the results from water samples collected from 2021-2023 and incorporate results from the currently deployed moorings if they are successfully retrieved in August 2023.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.654 | 0.650 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".