Fatty acids and glycerides are object recognition and carrying cues for foraging Camponotus modoc carpenter ants
Bibliographic record
Abstract
This data set is a supplement to an in review article titled: “Fatty acids and glycerides are object recognition and carrying cues for foraging Camponotus modoc carpenter ants”. Our full description of the methodology, results, and their interpretation can be found in the publication. Abstract from article: "During foraging and nest hygiene maintenance (removal of deceased nestmates), ants recognize objects for pickup and transport based on their surface chemicals. Diverse lipids are present on food items and deceased nestmates of ants, and the lipids oleic acid and 1,2-diolein are already well-known pickup cues. However, the effects of various lipid types on pickup behaviour by ants have not yet been rigorously compared. Using the carpenter ant, Camponotus modoc Wheeler (Hymenoptera: Formicidae), as a model species and pieces of perlite as inert objects for pickup by ants, we (1) compared the effects of fatty acids and glycerides as perlite coating on perlite pickup and transport by ants, (2) tested the effect of 1,2-diolein dose on perlite pickup, and (3) compared pickup behavior by ants in response to pickup cues that are wide-spread (oleic acid and 1,2 diolein), and commercially used in ant baits (soybean oil). Of 18 surface chemicals tested singly as perlite coating, 1,2-diolein, linoleic acid, oleic acid, trilinolenin, and triolein elicited the strongest perlite pickup behaviour. Increasing doses of 1,2-diolein correspondingly enhanced perlite pickup by ants. Oleic acid and 1,2-diolein as perlite coating prompted more perlite pickup by ants than soybean oil. Enriching soybean oil with oleic acid might enhance the pickup efficacy of granular baits by pest ants." We have uploaded our data and scripts as an R studio project. Code used to wrangle data, analyze data and generate plots can be accessed in the project folder by opening the project file. The project contains: ---Data Data files of laboratory experiments to assess ant consumption, colony growth, and mortality to amino acid formulations. ---Outputs Plots and csv files generated from data analysis. ---Scripts Scripts of R code used to wrangle data, conduct analyses, and generate outputs.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".