Bibliographic record
Abstract
Over the last century, the earth has seen unprecedented atmospheric concentrations contaminate our ecosystems due to human activity. Predictions state the introduction of carbon dioxide, methane, nitrous oxide, and chlorofluorocarbons (CFCs), will increase temperatures, and change the amount and location of precipitation causing more runoff. This could potentially result in disturbance events such as floods, to be more frequent and severe. This study aims to perform an assessment of the effects of a range of hypothetical climate changes on runoff in the North-east Pond River watershed, located in Newfoundland. To carry this out a watershed runoff model simulates runoff in the basin for current climatic conditions and for hypothetical climatic conditions that represent a range of possible climate changes (Bobba et al., 1997). The hypothetical changes in climate will showcase the effects of a 2oC increase in temperature on the total annual precipitation. This will then be compared to flood forecasting models to analyze how runoff will be affected by various climatic conditions, inducing unusual flooding events (Wijayarathne & Coulibaly, 2020). Previous studies have indicated the runoff sensitivity in watersheds to changes in temperatures which raises concerns as to the adverse effects this may cause in limiting water resources in the semi-arid regions in parts of Canada and the U.S. Thus, there is a need to increase the understanding of the sensitivity of water resources in Canadian watersheds to climate variability and climate change as effects of this magnitude on the North-east Pond River could have significant environmental implications.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".