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Record W4309207610 · doi:10.5038/1827-806x.51.3.2438

The nitrogen dynamics of Deer Cave, Sarawak, and the role of bat caves as biogeochemical sinks in Tropical Moist Forests

2022· article· en· W4309207610 on OpenAlexaff
Joyce Lundberg, Donald A. McFarlane, Guy Van Rentergem

Bibliographic record

VenueInternational Journal of Speleology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsCaveGuanoBiogeochemical cycleEnvironmental scienceEcologySink (geography)EcosystemHydrology (agriculture)GeographyBiologyGeology

Abstract

fetched live from OpenAlex

A better understanding of the role of bat caves as nitrogen sinks in tropical moist forest ecosystems can be expected to shed light on regional and spatial variability in nutrient recycling studies. We measured the nitrogen flux (in air and water) associated with a very large Chaerephon plicata bat colony in Deer Cave, Borneo, in the process generating a new, quantitative, estimate of the total bat population (774,828 ±48,320), and the first detailed modelling of an ammonia plume in a cave. Long-term storage of N does not occur in this wet cave. Our final budget numbers indicate that, of the daily input of N (i.e., ecologically-useful fixed-N in guano) to this cave, ammonia production is minor (and most of it is exported in water rather than air). The conclusion is that the export budget is dominated (perhaps as large as 94.4%) by microbial denitrification of fixed-N to diatomic N exported in air. Deer Cave thus acts as a nitrogen sink, potentially removing up to 39% of the ecologically-useful fixed-N from the total forest nitrogen budget over an area of hundreds of square kilometers.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.504

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.003
GPT teacher head0.200
Teacher spread0.197 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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