Bibliographic record
Abstract
In This Issue Bruce Osborne Not unsavoury for bats? Although the diet of bats has received little attention, it is thought to comprise mainly of butterflies and moths, with seasonal and location-dependent variations. One of the challenges with identifying the composition of bat diets is that studies have largely been restricted to the skeletal remains found in bat droppings, which may not always be easy to identify, and not to everyone’s taste! Using a molecular approach, Nolan et al., in this issue, first confirmed that the sample droppings were from the brown long-eared bat, Plecotus auratus, and that these contained the partially digested remains of the toxic cinnabar moth (Tyria jacobaeae). This finding was rather surprising, as it is the first time that this moth has been recorded in any insectivorous bat diet. As always, this work raises more questions than answers, including how can the bat deal with a toxic, unsavoury prey that only a few vertebrate species are able to feed on, and to what extent does the moth contribute to the bat diet? Could it have been a case of mistaken identity—bats have difficulties recognising visual cues—or was this just a chance event? It seems that the toxicity of cinnabar moths stems from the storage of compounds derived from the common ragwort, Senecio jacobea, with which it forms a specialist host relationship. The question then might be whether the moth had been feeding on ragwort tissues with relatively low toxin levels and/or the bat is able to metabolise these compounds? Certainly, the restricted availability of suitable prey does not seem to be a factor in the choice of diet in this study, given the plentiful food supply available to Plecotus auratus, and more research will be needed to resolve these issues. Managing drought Understandably, water management in Ireland has focused primarily on flooding and waterlogging risks, given the high annual rainfall and the often poor drainage systems. However, the droughts of 2018 and 2020 were reminders that water shortages are probably more common and have more impact than is often recognised. In addition, droughts are likely to increase in the future because of climate change. In a comparison of water management practices in Ireland with those in southwestern Ontario, Canada, a region that shares comparable challenges to those experienced in Ireland (but with more established institutional practices for managing droughts), Jobbova et al., in this issue, identify several key recommendations. These include the development of a culture of water conservation, a focus on catchment management and monitoring, and the establishment of drought management teams that include all relevant stakeholders and user groups. The authors also emphasise the importance of knowledge transfer between water managers in other jurisdictions. But will these recommendations be enough without significant investment in personnel and resources and the development of water conservation systems? Future conditions are also likely to be temporarily more dynamic and regionally more variable than those of the past, with extended periods of water deficits interspersed with extreme precipitation events. This will require a much more nuanced approach to the management of water resources and the development of best practice approaches that consider extreme fluctuations in water availability, rather than focusing specifically on drought management. Non-invasive monitoring of harbour seals Demographic investigations are often an undervalued but core requirement for many conservation efforts that include, for instance, assessments of trends in population size, variations in reproductive rates and how organisms interact with the environment. Traditional approaches using observers and/or marked individuals are often time consuming, subjected to identification errors (observer bias) and can potentially impact on the populations and individuals under investigation. In recent years a range of non-invasive methods for monitoring populations have been introduced, although many of these, including boat-and aerial-based surveys may not provide sufficient detail to identify individuals and individual behaviour. One novel approach to non-invasive monitoring is to use photographs that capture the natural markings and other external features associated with individuals and then to use these photo-IDs as the basis for identifying individuals in combination with the appropriate software. Using photographs from 173 land-based surveys Steinmetz et al., in this...
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.500 | 0.324 |
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; the direct Gemma label and the distilled Codex classifier 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".