Wildlife Population Welfare as Coherence Between Adapted Capacities and Environmental Realities: A Case Study of Threatened Lamprey on Vancouver Island
Bibliographic record
Abstract
Wildlife conservation lacks a well-accepted ethical foundation for population welfare. In this paper we propose a definition of wildlife population welfare and use a case study to suggest its value for species recovery planning. We define wildlife population welfare as coherence between the species’ adapted capacities and the realities of its current environment. We present a case study of the Cowichan Lake lamprey (Entosphenus macrostomus), a parasitic fish species endemic to three connected lakes in British Columbia, Canada. Individual-level welfare concerns were insufficient to inspire actions to protect this threatened species. The key threats to Cowichan Lake lamprey can be linked to anthropogenic changes and global threats such as climate change. Due to prevailing uncertainties and the inability to eliminate critical threats, the species recovery plan was focussed on securing critical environmental and social assets to meet evolved adaptations of lamprey while considering the needs of other species, including people. This assets focussed approach was well suited to developing consensus for action to enable a harm reduction perspective that recognizes that many of the threats cannot be eliminated but actions could be taken to enable the population to succeed by protecting critical environmental resources. This was consistent with our population welfare perspective which focusses on assets rather then deficits to help identify shared priorities for species recovery, conservation obligations and social expectations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".