Hemerobius humulinus Linnaeus 1758
Bibliographic record
Abstract
Hemerobius humulinus Linnaeus, 1758 Hemerobius humulinus Linnaeus, 1758: 550. Type Locality: Europe. Hemerobius apicalis Stephens, 1836: 110. Type Locality: United Kingdom (England: near London). Hemerobius obscurus Stephens, 1836: 108. Type Locality: United Kingdom (England: near London). Hemerobius subfasciatus Stephens, 1836: 111. Type Locality: United Kingdom (England: near London). Hemerobius affinis Wesmael, 1841: 216. Type Locality: Belgium (near Brussels [Bruxelles]). Hemerobius maculatus Wesmael, 1841: 215. Type Locality: Belgium (near Brussels [Bruxelles]). Hemerobius obliteratus Walker, 1853: 289. Type Locality: United States (Georgia). Hemerobius castaneae Fitch, 1855: 798. Type Locality: United States (New York or Illinois). Hemerobius tutatrix Fitch, 1855: 798. Type Locality: United States (New York). Hemerobius gossypii Ashmead, 1894: 27. Type Locality: United States (Georgia). Chrysopa karakurti Rossikov, 1904: 129. Type Locality: Uzbekistan (Surxondaryo: Chosh [Chash]). Hemerobius algonquinus Banks, 1924: 429. Type Locality: United States (New Hampshire: Rockingham: Hampton). Hemerobius obtusus Nakahara, 1954: 42. Type Locality: Japan (Tokyo: Mejiro). Hemerobius shikotanus Kuwayama, 1956: 77. Type Locality: Russia (Sakhalin: Kurile, Shikotan Island: Shakotan). Indian distribution: Meghalaya: East Khasi Hills Dist.: Shillong (hill station), Botanical Garden (locale); Elephant Falls (locale) (Ghosh 2000a: 25). World distribution: Albania, Armenia, Austria, Belarus, Belgium, Bosnia & Herzegovina, Bulgaria, Canada, China, Croatia, Czechia, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Guatemala, Hungary, India, Iran, Ireland, Italy, Japan, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Mexico, Moldova, Netherlands, North Korea, North Macedonia, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovenia, Spain, Sweden, Switzerland, Tajikistan, Turkey, Turkmenistan, Ukraine, United Kingdom, United States, Uzbekistan (Stephens 1836: 108; Wesmael 1841: 215; Fitch 1854: 798; Currie 1904: 84; Navás 1936a: 50; Kuwayama 1954: 99; Kuwayama 1956: 78; Tjeder 1972: 22; Popov 1977: 12; Monserrat 1991: 67; Zakharenko & Krivokhatsky 1993: 52; Monserrat 1996b: 429; Ghosh 2000a: 25; Monserrat 2000: 64; H. Aspöck et al. 2001: 368, 371, 375; Oswald et al. 2002: 579; Devetak & Jaksic 2003: 48; Oswald & Makarkin 2004: 151; Canbulat 2007: 42; Badano & Letardi 2010: 85; Klokočovnik et al. 2014: 234; Devetak et al. 2015: 22; Devetak et al. 2019a: 177; Oswald 2024a).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".