Supporting wild bee development with a bacterial symbiont
Bibliographic record
Abstract
AIMS: Wild bees foster diverse microbiota that may determine survival success of developing larvae. Here, we compare survivorship and microbial communities of Ceratina calcarata small carpenter bees reared from eggs across three treatments: maternally collected control provisions with diverse microbiota, sterile provisions, and probiotic provisions supplemented with a beneficial symbiont, Apilactobacillus kunkeei. METHODS AND RESULTS: Survival probability and adult masses differed across treatments, with the probiotic treatment resulting in highest survivorship and masses. By comparing the bacterial (16S rRNA), fungal (ITS), and plant (rbcL) communities of adults reared across treatments, we characterized distinct microbial communities across each that suggest the microbiome may be sensitive to microbial succession and competition. CONCLUSIONS: We describe positive implications for the usage of probiotics on wild bees. Furthermore, the sensitivity of bee microbiota's relationships to their host, floral resources, and the environment suggests that holistic approaches best encapsulate the complex network of interactions between bees and their microbes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.000 |
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.
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".