Bibliographic record
Abstract
Some eight years ago Martin Wikelski and I started musing about how physiological knowledge and tools could be used to understand conservation problems. We were certainly not the first to do so, but we were able to codify the idea by coin ing the term ‘conservation physiology’ (Wikelski and Cooke, 2006), which has been embraced by many. When the leadership at the Society of Experimental Biology (SEB), including Tony Farrell, Craig Franklin, and Paul Hutchinson, embarked on a plan to launch a new journal, the topic of conservation physiology quickly rose to the top, given its timeliness and potential to engage both plant and animal researchers. That was nearly two years ago. Today I am thrilled to launch the second volume of the journal Conservation Physiology, published jointly by Oxford University Press (OUP) and the SEB. Here I briefly reflect on the first year (2013) of contributions to Conservation Physiology and consider what can be expected in 2014 and beyond. The Conservation Physiology submission portal opened quietly in December of 2012 and, with negligible advertising, we started receiving submissions. The inaugural paper (Cooke et al., 2013) was published in March, with the official launch of the journal occurring with great fanfare at the SEB Annual Meeting in Valencia, Spain in July. As of December 2013, Volume 1 had over 30 papers published, with another handful of accepted articles in production. The content is remarkably diverse, spanning taxa including plants (Funk,
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".