Annual Scientific Meeting 2022 Conference Book of Abstracts
Bibliographic record
Abstract
Before the 1990s, eelgrass in Eastern James Bay was extensive, lush, and supported a productive and predictable goose hunt that was a centerpiece of coastal Cree culture and food security. Eelgrass declined catastrophically in the 1990s, at a time when Hydro Quebec had modified hydrology through river modification and as climate change impacts on the marine environment accelerated. Eelgrass and the associated goose hunt have not recovered. On behalf of a multidisciplinary research team and partnership with Cree communities, I present results of the Eeyou Coastal Habitat Comprehensive Research Project that aimed to identify the main factors affecting eelgrass along the eastern coast of James Bay. Eelgrass first began to decline in Chisasibi in the 1980s, which we attribute to the development of La Grande River. The onset of very early ice breakup and warm earlysummer water temperatures in the late 1990s accelerated the eelgrass decline in Chisasibi and triggered declines along the entire coast. Eelgrass today are shorter, sparse, and limited to shallow water. Insufficient light during early summer due to water color is a general problem impeding recovery. In coastal areas that lost aayoshtinuukticj, recovery is also impeded by feedbacks associated with sediment resuspension. Near La Grande River, eelgrass biomass is negatively affected by high flows and warmer spring water temperatures. During the 1970s, healthy eelgrass provided very important feeding areas for migrating geese. Because eelgrass has persisted, perhaps it can recover, but much depends on how the climate varies in the coming years and future coastal management.
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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) | 0.666 | 0.672 |
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".