Demonstration of a methodology for setting ecological flow and water level targets
Bibliographic record
Abstract
This paper demonstrates a methodology developed through applying and refining Lake Simcoe Region Conservation Authority (LSRCA) and Bradford’s (2011) framework for setting ecological flow and water level targets. Each step of the methodology is described along with an example application within Lovers Creek subwatershed. The methodology requires subwatershed objectives and habitat specialists to be defined. Study nodes are selected and the reference streamflow regime is characterized along with the hydrological alteration that has occurred, or is expected to occur under a given scenario. Potential ecological responses to the various hydrologic alterations are then identified. Methods for setting overall ecosystem health and specific ecological objective flow targets are discussed and demonstrated. The targets are then integrated into a flow regime for each study node and a process for using this information for decision-making is suggested. The targets developed using the methodology presented in this paper are mainly limited by the accuracy of the hydrologic model and the quantified flow magnitudes. Recommendations for improving these components of the assessment are made. The unique approach presented in this paper provides explicit steps for developing flow targets for subwatersheds within Southern Ontario and beyond. This research contributes toward the advancement of ecological flow assessment within Southern Ontario, which provides opportunities for enhanced protection and restoration of ecosystem health across the Province.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".