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Record W4415930981 · doi:10.1101/2025.11.03.686388

Using insects for sustainable waste management of superabundant animals

2025· preprint· W4415930981 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Language
FieldAgricultural and Biological Sciences
TopicInsect Utilization and Effects
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsFrassLarvaSustainabilityGooseWildlifeFecesBiodegradable wasteSterilization (economics)

Abstract

fetched live from OpenAlex

Abstract Management of fecal waste from superabundant wildlife in urban areas is a key environmental and public health concern, yet even in developed countries, sustainable solutions that implement circular economy principles are still lacking. We tested the potential of black soldier fly (BSF) larvae for valorizing organic waste of the widespread Canada goose: converting droppings into larval protein while yielding frass fertilizer. Larvae were able to survive, develop, and degrade the goose feces. For instance, larvae degraded 56% of goose feces compared to 63% of a control diet. Sterilization of feces reduced the performance of larvae. We tested the fertilization properties of the insect frass on an aquatic plant (duckweed) and we found growth enhancement of 32% at 10 g·L -1 compared to a standard Hoagland’s media. Our results provide insights into how to sustainably manage urban fecal waste from overabundant species while producing protein and a nutrient-rich fertilizer for plants.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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