Conservation physiology and the quest for a ‘good’ Anthropocene
Bibliographic record
Abstract
It has been proposed that we are now living in a new geological epoch known as the Anthropocene, which is specifically defined by the impacts that humans are having on the Earth's biological diversity and geology. Although the proposal of this term was borne out of an acknowledgement of the negative changes we are imparting on the globe (e.g. climate change, pollution, coastal erosion, species extinctions), there has recently been action amongst a variety of disciplines aimed at achieving a 'good Anthropocene' that strives to balance societal needs and the preservation of the natural world. Here, we outline ways that the discipline of conservation physiology can help to delineate a hopeful, progressive and productive path for conservation in the Anthropocene and, specifically, achieve that vision. We focus on four primary ways that conservation physiology can contribute, as follows: (i) building a proactive approach to conservation; (ii) encouraging a pragmatic perspective; (iii) establishing an appreciation for environmental resilience; and (iv) informing and engaging the public and political arenas. As a collection of passionate individuals combining theory, technological advances, public engagement and a dedication to achieving conservation success, conservation physiologists are poised to make meaningful contributions to the productive, motivational and positive way forward that is necessary to curb and reverse negative human impact on the environment.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".