MétaCan
Menu
Back to cohort
Record W2504762644 · doi:10.1385/0-89603-515-8:487

Formulations of Biopesticides

2003· book-chapter· en· W2504762644 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.

Bibliographic record

VenueHumana Press eBooks · 2003
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicEntomopathogenic Microorganisms in Pest Control
Canadian institutionsSimon Fraser UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiopesticideBiotechnologyBiological pest controlBiochemical engineeringBiologyRisk analysis (engineering)BusinessEngineeringPesticideEcology

Abstract

fetched live from OpenAlex

A large number of factors can potentially affect the economic feasibility of any given biological control product. These include the impact on the target pest, market size and spectrum of pests affected by the biocontrol agent, vari ability of field performance, costs of production, and a number of technologi cal challenges, including fermentation, formulation, and delivery systems (1–42). Selection of the appropriate formulations that can improve product sta bility and viability may reduce inconsistency of field performance of many potential biological control agents ((2), 5, 6). It has been indicated that slow progress in research on formulation and delivery systems is a major hurdle to the development of biopesticide products ((1),(7)). This chapter summarizes the efforts and successes toward formulation of biocontrol products for use against diseases (biofungicides), weeds (bioherbicides), and insect pests (bioinsecticides). The discussion emphasizes the use of bacteria, fungi, and viruses as the agents. Information on formulation of other important biocontrol agents, such as nematodes, can be found elsewhere (8).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.954
Threshold uncertainty score1.000

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)0.0010.000

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 head0.052
GPT teacher head0.228
Teacher spread0.176 · 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