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A Field-Tested Protocol for the Measurement and Mapping of Bat Guano Deposition Rate in Caves

2024· article· en· W4401520109 on OpenAlexaff
Guy Van Rentergem, Joyce Lundberg, Donald A. McFarlane, Warren Roberts

Bibliographic record

VenueActa Chiropterologica · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsGuanoCaveDeposition (geology)Context (archaeology)EcologyEnvironmental sciencePhysical geographyGeographyArchaeologyBiologyPaleontologySediment

Abstract

fetched live from OpenAlex

Measurement of bat guano deposition rate in caves can be an important research tool for estimation of colony size, for monitoring the record of long-term bat population trends, and for allied studies of guano invertebrate ecology, environmental contaminants, and paleoecology. However, previously published methodologies have lacked consistency. In the context of our recent studies of insectivorous bat guano deposition rates in Deer Cave, Sarawak, Borneo, we review some past studies and offer suggestions for best practises, along with proposed experimental design considerations for future studies (e.g., design of guano catchers, optimum deployment of catchers in relation to specific site characteristics, data reporting standards, and examples of mapping techniques). Consistent techniques will facilitate inter-site comparisons, and determination of intra-site changes over time. As a case study, and the first publication of detailed mapping of spatial variability in guano deposition rates, we present spatially explicit guano deposition rates for Deer Cave, ranging up to 88 g (dry weight)/m2/day in the main cave, and up to 540 g (dry weight)/m2/day in the northern extension.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.114

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.265
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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