Identifying a suite of surrogate freshwaterscape fish species: a case study of conservation prioritization in Ontario's Far North, Canada
Bibliographic record
Abstract
Abstract Freshwater ecosystems are among the most threatened ecosystems on the planet, yet freshwater fish species are frequently overlooked in conservation planning initiatives. Ontario's Far North (OFN) in Canada is at present one of the largest relatively intact landscapes in North America with 11% of the total area covered by freshwater ecosystems, not including wetlands. Resource development is being planned for OFN but due to the paucity of data on fish species and the freshwater ecosystems they inhabit, the freshwaterscape is largely being overlooked. Given the importance of freshwater resources in OFN, existing information on fish species in OFN was compiled and assessed using the Landscape Species Approach (LSA) on this freshwaterscape. The LSA is a species‐based conservation planning tool developed for terrestrial conservation that is constructed around the identification of focal species for a given landscape. An analysis of 14 large‐bodied candidate freshwater species, including their area requirements, habitat use, ecological function, socio‐economic function and vulnerability to threats was used to identify three freshwaterscape species: lake sturgeon, lake trout, and walleye. The identification of these species and their ecological requirements suggests a starting place for research, management, and conservation of freshwater resources in OFN before large‐scale landscape changes. Copyright © 2015 John Wiley & Sons, Ltd.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".