Effects of an inoculant containing <i>Lentilactobacillus hilgardii</i> (CNCM-I-4785) on the microbiome, fermentation, and aerobic stability of corn silage stored for long term at different incubation temperatures
Bibliographic record
Abstract
Whole-plant corn untreated (Control) or treated with an inoculant ( Lentilactobacillus buchneri, Lentilactobacillus hilgardii, and Pediococcus pentosaceus, INO) was ensiled for 210 days in mini-silos at constant (MS-C) or variable temperature (MS-V) or for 220 days in bunkers. Bunker samples were collected at 50 (D50) and 150 (D150) cm below silo surface. Samples from MS-V and MS-C had similar pH and concentrations of lactic acid, propionic acid, 1,2-propanediol, and ethanol, but different acetic acid content ( P = 0.009). Additionally, MS-V exhibited greater bacterial ( P = 0.002) and fungal ( P = 0.011) richness than MS-C. Inoculation decreased ( P < 0.001) lactic acid levels while increasing ( P < 0.05) acetic acid, 1,2-propanediol, and fungal richness in both mini-silos and bunker. In bunkers, samples collected from D50 had a lower aerobic stability ( P < 0.05) than D150, but inoculation increased ( P < 0.05) aerobic stability compared to Control, regardless of sampling depth. The storage temperature of mini-silos did not markedly impact the fermentation profile or fungal community. Overall, inoculation increased acetic acid production and fungal diversity in mini-silos, regardless of the storage temperature, and in bunkers, irrespective of sampling the depth, improving the aerobic stability of D50 and D150 bunker silages after long-term ensiling.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".