Bibliographic record
Abstract
2.3. Pimplinae Collections We collected pimplines using 30 Malaise traps (a form a flight interception trap) distributed at 15 broad elevation sites throughout the transect, with two replicate traps at each site. Malaise traps are one of the most efficient methods for sampling Ichneumonidae and are widely used in ecological studies of insects [94]. Traps were set at ground level, with the collecting head 1.5 m above the ground. Traps were placed at least 50 m from the road and the 15 sites were roughly spaced at 100 m to 200 m elevation intervals. At each elevational site, the two traps were placed at least 50 m apart from each other to ensure that neither trap affected the catch of the other and to sample different environmental space [65]. Trap collecting bottles (1 L capacity) contained 98% ethanol for preservation of the sampled material and were replaced monthly (after 30 days of collecting). The samples were collected during both the rainy hot season, from December 2014 to February 2015, and the dry cooler season from June to August 2015, totaling 180 Malaise trap months. These months were chosen to represent the opposite environmental extremes throughout the year to capture seasonal variations in species composition, but also to encompass the warmest, wettest months when insect activity is expected to be highest (December–February). Insects were preserved in 98% ethanol and stored in plastic bottles. Sample sorting was performed in the laboratory using a stereoscopic microscope. Identification was initially carried out according to subfamily by D.R.R.F. and D.G.P. following [92]. Then, the Pimplinae were identified according to genus following [89]. Thereafter, morphospecies were identified by D.G.P. in conjunction with I.E.S., and where possible, species (using specific bibliography and large reference collections of neotropical Darwin wasps in the Biodiversity Unit, University of Turku, Finland). All the researchers involved in identification were experienced in neotropical ichneumonid taxonomy. The use of morphospecies (i.e., individuals sorted based on phenotypic characteristics) as surrogates for species is widely used in the estimation of species richness for comparisons over time and space [95, 96]. Although the designation of morphospecies can lead to the split of a single species into many different morphospecies (“splitting”) or aggregation of different species into a single species (“lumping”), it is often the only way to assess species diversity in groups that have not been fully described [95, 96]. Collections were performed under license number 21409-10 (Ministério do Meio Ambiente—MMA; Instituto Chico Mendes de Conservação da Biodiversidade—ICMBio; Sistema de Autorização e Informação em Biodiversidade—SISBIO) to Ricardo Ferreira Monteiro. The sampled material is deposited at the following Brazilian entomological collections: Invertebrate Collection of Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil (INPA), (curator: Marcio L. Oliveira); Museu de Zoologia da Universidade de São Paulo, São Paulo, Brazil (MZUSP), (curator: Gabriela P. Camacho); and Taxonomic Collection of the Departamento de Ecologia e Biologia Evolutiva from Universidade Federal de São Carlos, São Carlos, Brazil (DCBU), (curator: Angelica M. Penteado-Dias).
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".