Long-Term Eelgrass Habitat Change and Associated Human Impacts on the West Coast of Canada
Bibliographic record
Abstract
Nahirnick, N.K.; Costa, M.; Schroeder, S., and Sharma, T., 2020. Long-term eelgrass habitat change and associated human impacts on the west coast of Canada. Journal of Coastal Research, 36(1), 30–40. Coconut Creek (Florida), ISSN 0749-0208.Eelgrass (Zostera marina) forms a critical nearshore marine habitat in temperate coastal ecosystems. For three small estuaries in the Southern Gulf Islands of British Columbia, changes in eelgrass area coverage and shape index (over the period of 1932–2016) were assessed using historical aerial photographs and unoccupied aerial system (UAS) imagery. In addition, changes in eelgrass area and shape index were evaluated in relation to landscape-level coastal environmental indicators, namely shoreline activities and alterations and residential housing density. All three eelgrass meadows showed a deteriorating trend in eelgrass condition; on average, eelgrass area coverage decreased by 45.1%, while meadow complexity as indicated by the shape index increased by 66.3%. Shoreline activities (boats, docks, log booms, and armoring) and residential housing density increased markedly at all sites over the study period and were strongly correlated to eelgrass area coverage and shape index. Changes in these landscape-level indicators over this period corroborate the observed decline in eelgrass habitat condition, because they suggest an overall deterioration of coastal environmental health in the Salish Sea due to increased use of the coastal zone, as well as declines in water quality due to urbanization.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".